research paper on language loss

Research Methods in Language and Education pp 99–111 Cite as

Researching Language Loss and Revitalization

• Leena Huss 5  
• Reference work entry
• First Online: 28 May 2017

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Part of the book series: Encyclopedia of Language and Education ((ELE))

Language loss refers to a societal or individual loss in the use or in the ability to use a language, implying that another language is replacing it. Revitalization, in turn, is commonly understood as giving new life and vigor to a language that has been decreasing in use and is today a rapidly growing field of study. Both fields are highly multidisciplinary, drawing from linguistics, sociology, education, psychology, anthropology, political science, and other disciplines.

Since the 1990s, the research interest in endangered languages and consciousness of the need to contribute to their survival have grown among researchers, and numerous studies have been undertaken to present what has been done to curb language decline and to explain why some languages survive and others do not. Researchers have also tried to pinpoint the most relevant factors and the ways in which they interact. Still, to establish language revitalization more firmly as an independent field of study, more research and theorization are needed.

Many revitalization efforts are connected with ethnic revival movements as revitalization of the language is often seen as a crucial part of the overall ethnic revival. As a reaction to former forced assimilation and oppression, revitalization movements are often seen as ways to healing, redress, and empowerment. Therefore, a growing part of revitalization research is today being done by, or in close collaboration with, researchers and other members coming from the language communities themselves.

The chapter deals with research approaches in the field of language loss and revitalization, as well as challenges faced by scholars in this area.

• Assimilation
• Language maintenance
• Language shift
• Language revitalization
• Minority languages
• Indigenous languages
• Tornedalians

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Huss, L. (2017). Researching Language Loss and Revitalization. In: King, K., Lai, YJ., May, S. (eds) Research Methods in Language and Education. Encyclopedia of Language and Education. Springer, Cham. https://doi.org/10.1007/978-3-319-02249-9_7

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1,500 endangered languages could disappear by the end of the century

With 1,500 languages at risk of being lost, how can we help preserve them? Image:  Unsplash/Towfiqu barbhuiya

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• Around 1,500 known languages may no longer be spoken by the end of this century.
• Current levels of language loss could triple in the next 40 years.
• Greater education and mobility marginalize some minor languages.
• One language per month could disappear, without intervention.

There are 7,000 documented languages currently spoken across the world, but half of them could be endangered , according to a new study. It is predicted that 1,500 known languages may no longer be spoken by the end of this century. Researchers from The Australian National University (ANU) analyzed thousands of languages to identify factors that put endangered ones at risk. The findings highlight a link between higher levels of schooling and language loss, as regionally dominant languages taught in class often overshadow indigenous tongues.

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A second factor exacerbating the threat to endangered languages is the density of roads in an area. While contact with other languages can help preserve indigenous ones, exposure to the wider world may not.

“We found that the more roads there are, connecting country to city, and villages to towns, the higher the risk of languages being endangered. It’s as if roads are helping dominant languages ‘steam roll’ over other smaller languages,” said Professor Lindell Bronham, co-author of the study.

Lost language diversity

The factors identified by the study could help explain why just a handful of languages dominate global communication.

Mandarin Chinese has the most native speakers, which is unsurprising given China’s huge population, but English is the world’s most widely used language with around 1.35 billion speakers. The study, published in Nature, Ecology and Evolution , shows the extent to which the world’s language diversity is under threat. It estimates the equivalent of one language is currently lost within every three-month period. But levels of language loss could actually triple in the next 40 years, with at least one language per month disappearing unless measures are taken. “When a language is lost or is ‘sleeping’ as we say for languages that are no longer spoken, we lose so much of our human cultural diversity,” said Professor Bromham.

“Many of the languages predicted to be lost this century still have fluent speakers, so there is still the chance to invest in supporting communities to revitalize indigenous languages and keep them strong for future generations.”

Can technology help save indigenous languages?

While past studies have blamed the digital realm for causing the demise of some indigenous dialects - by focusing attention on a few major languages at the expense of smaller ones - today’s tech-entwined world could hold a solution. There are Internet sites and apps aplenty to help new speakers learn languages like Spanish, English and Mandarin, but these now extend to specialist apps designed to teach endangered languages or help preserve them. Ma! Iwaidja, for example, is an app that enables those working with speakers of the Iwaidja indigenous Australian language to record words, phrases and translations. It also contains a dictionary and a word maker to help users tackle grammar and syntax.

Another initiative is the Rosetta Project , a global collaboration of language specialists and native speakers working to build an open-access digital library of human languages. The collection contains around 100,000 pages of documents and recordings for more than 2,500 languages microscopically etched on nickel disks for long-term storage. The project draws attention to the “drastic and accelerated loss of the world’s languages” and could help preserve many endangered and “sleeping” languages for future generations.

The UNESCO International Decade of Indigenous Languages (IDIL2022-2032) , which begins this year, also aims to engage the global community with the critical issue of language loss.

The 10-year initiative continues the work of the UN’s 2019 International Year of Indigenous Languages. As part of its Global Action Plan, IDIL2022-2032, it is creating a network of international stakeholders focused on protecting the rights of indigenous people to revitalize and preserve their languages.

The COVID-19 pandemic and recent social and political unrest have created a profound sense of urgency for companies to actively work to tackle inequity.

The Forum's work on Diversity, Equality, Inclusion and Social Justice is driven by the New Economy and Society Platform, which is focused on building prosperous, inclusive and just economies and societies. In addition to its work on economic growth, revival and transformation, work, wages and job creation, and education, skills and learning, the Platform takes an integrated and holistic approach to diversity, equity, inclusion and social justice, and aims to tackle exclusion, bias and discrimination related to race, gender, ability, sexual orientation and all other forms of human diversity.

The Platform produces data, standards and insights, such as the Global Gender Gap Report and the Diversity, Equity and Inclusion 4.0 Toolkit , and drives or supports action initiatives, such as Partnering for Racial Justice in Business , The Valuable 500 – Closing the Disability Inclusion Gap , Hardwiring Gender Parity in the Future of Work , Closing the Gender Gap Country Accelerators , the Partnership for Global LGBTI Equality , the Community of Chief Diversity and Inclusion Officers and the Global Future Council on Equity and Social Justice .

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The Importance of Maintaining Native Language

The United States is often proudly referred to as the “melting pot.” Cultural diversity has become a part of our country’s identity. However, as American linguist, Lilly Wong Fillmore, pointed out in her language loss study, minority languages remain surprisingly unsupported in our education system (1991, p. 342). Although her research was conducted more than twenty years ago, this fact still rings true. Many non-minority Americans are not aware of the native language loss that has become prevalent in children of immigrant parents. While parents can maintain native language, children educated in U.S. schools quickly lose touch with their language heritage. This phenomenon, called subtractive bilingualism, was first discovered by psychologist Wallace Lambert, in his study of the language acquisition of French-Canadian children. The term refers to the fact that learning a second language directly affects primary language, causing loss of native language fluency (Fillmore, 1991, p. 323). This kind of language erosion has been integral to the narrative of this country for some time. Many non-minority Americans can trace their family tree back to a time when their ancestors lost fluency in a language that was not English. Today, due to the great emphasis on assimilation into the United States’ English-speaking culture, children of various minorities are not only losing fluency, but also their ability to speak in their native language, at all (Fillmore, 1991, p. 324).

The misconceptions surrounding bilingual education has done much to increase the educational system’s negative outlook on minority languages. In Lynn Malarz’s bilingual curriculum handbook, she states that “the main purpose of the bilingual program is to teach English as soon as possible and integrate the children into the mainstream of education” (1998). This handbook, although written in 1998, still gives valuable insight into how the goals of bilingual education were viewed. Since English has become a global language, this focus of bilingual education, which leads immigrant children to a future of English monolingualism, seems valid to many educators and policymakers. Why support minority languages in a country where English is the language of the prosperous? Shouldn’t we assimilate children to English as soon as possible, so that they can succeed in the mainstream, English-speaking culture? This  leads us to consider an essential question: does language loss matter? Through the research of many linguists, psychologists, and language educators, it has been shown that the effect of native language loss reaches far. It impacts familial and social relationships, personal identity, the socio-economic world, as well as cognitive abilities and academic success. This paper aims to examine the various benefits of maintaining one’s native language, and through this examination, reveal the negative effects of language loss.

Familial Implications

The impact of native language loss in the familial sphere spans parent-child and grandparent-grandchild relationships, as well as cultural respects. Psychologists Boutakidis, Chao, and Rodríguez, (2011) conducted a study of Chinese and Korean immigrant families to see how the relationships between the 9th-grade adolescences and their parents were impacted by native language loss. They found that, because the adolescents had limited understanding and communicative abilities in the parental language, there were key cultural values that could not be understood (Boutakidis et al., 2011, p. 129). They also discovered there was a direct correlation between respect for parents and native language fluency. For example, honorific titles, a central component of respect unique to Chinese and Korean culture, have no English alternatives (p.129). They sum up their research pertaining to this idea by stating that “children’s fluency in the parental heritage language is integral to fully understanding and comprehending the parental culture” (Boutakidis et al., 2011, p. 129). Not only is language integral to maintaining parental respect, but also cultural identity.

In her research regarding parental perceptions of maintaining native language, Ruth Lingxin Yan (2003) found that immigrant parents not only agree on the importance of maintaining native language, but have similar reasoning for their views. She discovered that maintaining native language was important to parents, because of its impact on heritage culture, religion, moral values, community connections, and broader career opportunities.

Melec Rodriguez, whose parents immigrated to the United States before he was born, finds that his native language loss directly impacts his relationship with his grandparents. Rodriguez experienced his language loss in high school. He stated that due to his changing social group and the fact that he began interacting with his family less, he found himself forgetting “uncommon words in the language.” His “struggle to process information” causes him to “take a moment” to “form sentences in [his] mind during conversations” (M. Rodriguez, personal communication, Nov. 3, 2019). Of his interactions with his grandparents, who have a limited understanding of English, he stated:

“I find very often that I simply cannot think of a way to reply while conveying genuine emotion, and I know they feel I am detached at times because of that. I also struggle to tell exciting stories about my experiences and find it hard to create meaningful conversations with family” (M. Rodriguez, personal communication, Nov. 3, 2019).

Rodriguez’s native language loss creates a distinct communicative barrier between him and his grandparents, causing him difficulty in genuine connection building. Although this is a relatively obvious implication of native language loss, it is nonetheless a concerning effect.

Personal Implications

Native language, as an integral part of the familial sphere, also has strong connections on a personal level. The degree of proficiency in one’s heritage language is intrinsically connected to self-identity. The Intercultural Development Research Association noted this connection, stating that “the child’s first language is critical to his or her identity. Maintaining this language helps the child value his or her culture and heritage, which contributes to a positive self-concept. (“Why Is It Important to Maintain the Native Language?” n.d.). Grace Cho, professor and researcher at California State University, concluded “that [heritage language] development can be an important part of identity formation and can help one retain a strong sense of identity to one's own ethnic group” (Cho, 2000, p. 369). In her research paper, she discussed the “identity crisis” many Korean American students face, due to the lack of proficiency they have in their heritage language (p. 374). Cho found that students with higher levels of fluency could engage in key aspects of their cultural community, which contributed greatly to overcoming identity crises and establishing their sense of self (p. 375).

Social Implications

Native language loss’ connections to family relationships and personal identity broaden to the social sphere, as well. Not only can native language loss benefit social interactions and one’s sense of cultural community, it has large-scale socioeconomic implication. In Cho’s study (2000) she found that college-aged participants with Korean ancestry were faced with many social challenges due to limited fluency in Korean. Participants labeled with poor proficiency remarked on the embarrassment they endured, leading them to withdraw from social situations that involved their own ethnic group (p. 376). These students thus felt isolated and excluded from the heritage culture their parents actively participated in. Native language loss also caused students to face rejection from their own ethnic communities, resulting in conflicts and frustration (p. 377). Participants that did not complain of any conflict actively avoided their Korean community due to their lack of proficiency (p. 378). Participants who were labeled as highly proficient in Korean told of the benefits this had, allowing them to “participate freely in cultural events or activities” (p. 374). Students who were able to maintain their native language were able to facilitate meaningful and beneficial interactions within their cultural community.

Melec Rodriguez made similar comments in his experience as a Spanish and English- speaking individual. Although his native language loss has negatively affected his familial relationships, he has found that, in the past, his Spanish fluency “allowed for a greater social network in [his] local community (school, church, events) as [he] was able to more easily understand and converse with others” (M. Rodriguez, personal communication, Nov. 3, 2019). As this research suggests, native language fluency has a considerate influence on social interactions. Essentially, a lack of fluency in one’s native language creates a social barrier; confident proficiency increases social benefits and allows genuine connections to form in one’s cultural community.

Benefits to the Economy

Maintaining native language not only benefits personal social spheres, but also personal career opportunities, and thereby the economy at large. Peeter Mehisto and David Marsh (2011), educators central to the Content and Language Integrated Learning educational approach, conducted research into the economic implications of bilingualism. Central to their discussion was the idea that “monolingualism acts as a barrier to trade and communication” (p. 26). Thus, bilingualism holds an intrinsic communicative value that benefits the economy. Although they discovered that the profits of bilingualism can change depending on the region, they referred to the Fradd/Boswell 1999 report, that showed Spanish and English-speaking Hispanics living in the United States earned more than Hispanics who had lost their Spanish fluency (Mehisto & Marsh, 2011, p. 22). Mehisto and Marsh also found that bilingualism makes many contributions to economic growth, specifically “education, government, [and] culture…” (p. 25). Bilingualism is valuable in a society in which numerous services are demanded by speakers of non-English languages. The United States is a prime example of a country in which this is the case.

Increased Job Opportunites

Melec Rodriguez, although he has experienced native language loss, explained that he experienced increased job opportunities due to his Spanish language background. He stated:

“Living in south Texas, it is very common for people to struggle with either English or Spanish, or even be completely unable to speak one of the languages. There are many restaurants or businesses which practice primarily in one language or the other. Being bilingual greatly increased the opportunity to get a job at many locations and could make or break being considered as a candidate” (M. Rodriguez, personal communication, Nov. 3, 2019).

Rodriguez went on to explain that if he were more confident in his native language, he would have been able to gain even more job opportunities. However, as his language loss has increased through the years, Spanish has become harder to utilize in work environments. Thus, maintaining one’s native language while assimilating to English is incredibly valuable, not only to the economy but also to one’s own occupational potential.

Cognitive and Academic Implications

Those who are losing native language fluency due to English assimilation are missing out on the cognitive and academic benefits of bilingualism. The Interculteral Development Research Association addresses an important issue in relation to immigrant children and academic success. When immigrant children begin at U. S. schools, most of their education is conducted in English. However, since these students are not yet fluent in English, they must switch to a language in which they function “at an intellectual level below their age” (“Why Is It Important to Maintain the Native Language?” n.d.). Thus, it is important that educational systems understand the importance of maintaining native language. It is also important for them to understand the misconceptions this situation poses for the academic assessments of such students.

In Enedina Garcia-Vazquez and her colleague's (1997) study of language proficiency’s connection to academic success, evidence was found that contradicted previous ideas about the correlation. The previous understanding of bilingualism in children was that it caused “mental confusion,” however, this was accounted for by the problematic methodologies used (Garcia- Vazquez, 1997, p. 395). In fact, Garcia-Vazquez et al. discuss how bilingualism increases “reasoning abilities” which influence “nonverbal problem-solving skills, divergent thinking skills, and field independence” (p. 396). Their study of English and Spanish speaking students revealed that proficiency in both languages leads to better scores on standardized tests (p. 404). The study agreed with previous research that showed bilingual children to exceed their monolingual peers when it came to situations involving “high level…cognitive control” (p. 396). Bilingualism thus proves to have a distinct influence on cognitive abilities.

Mehisto and Marsh (2011) discuss similar implications, citing research that reveals neurological differences in bilingual versus monolingual brains. This research indicates that the “corpus callosum in the brain of bilingual individuals is larger in area than is the case for monolinguals” (p. 30). This proves to be an important difference that reveals the bilingual individual’s superiority in many cognitive functions. When it comes to cognitive ability, Mehisto and Marsh discuss how bilinguals are able to draw on both languages, and thus “bring extra cognitive capacity” to problem-solving. Not only can bilingualism increase cognitive abilities, but it is also revealed to increase the “cognitive load” that they are able to manage at once (p.30). Many of the academic benefits of bilingualism focus on reading and writing skills. Garcia-Vazquez’s study focuses on how students who were fluent in both Spanish and English had superior verbal skills in both writing and reading, as well as oral communication (p. 404). However, research indicates that benefits are not confined to this area of academics. Due to increased cognition and problem-solving skills, research indicates that bilingual individuals who are fluent in both languages achieved better in mathematics than monolinguals, as well as less proficient bilinguals (Clarkson, 1992). Philip Clarkson, a mathematics education scholar, conducted one of many studies with students in Papua New Guinea. One key factor that Clarkson discovered was the importance of fluency level (p. 419). For example, if a student had experienced language loss in one of their languages, this loss directly impacted their mathematical competence. Not only does Clarkson’s research dissuade the preconceived notions that bilingualism gets in the way of mathematical learning, it actually proves to contribute “a clear advantage” for fluent bilingual students (p. 419). Clarkson goes on to suggest that this research disproves “the simplistic argument that has held sway for so long for not using languages other than English in Papua New Guinea schools” (p. 420). He thus implies the importance of maintaining the native language of the students in Papua New Guinea since this bilingual fluency directly impacts mathematical competency.

Both Garcia-Vazquez et al. and Mehisto and Marsh reveal how proficiency in two languages directly benefits a brain’s functions. Their research thus illustrates how maintaining one’s native language will lead to cognitive and academic benefits. Clarkson expands on the range of academic benefits a bilingual student might expect to have. It is important to note that,  as Clarkson’s research showed, the fluency of a bilingual student has much influence on their mathematical abilities. Thus, maintaining a solid fluency in one’s native language is an important aspect of mathematical success.

Suggested Educational Approach

The acculturation that occurs when immigrants move to the United States is the main force causing language loss. Because of the misconceptions of bilingual education, this language loss is not fully counteracted. Policymakers and educators have long held the belief that bilingual education is essentially a “cop-out” for immigrants who do not wish to assimilate to the United States’ English-speaking culture (Fillmore, 1991, p. 325). However, bilingual education is  central to the maintenance of native language. Due to the misconceptions and varied views on this controversial subject, there are two extremes of bilingual education in the United States. In Malarz’s (1998) curriculum handbook, she explains the two different viewpoints of these approaches. The first pedological style’s goal is to fully assimilate language-minority students to English as quickly and directly as possible. Its mindset is based on the idea that English is the language of the successful, and that by teaching this language as early as possible, language- minority children will have the best chance of prospering in mainstream society. However, this mindset is ignorant of the concept of subtractive bilingualism, and thus is not aware that its approach is causing native language loss. The second approach Malarz discusses is the bilingual education that places primary importance on retaining the student’s heritage culture, and thereby, their native language. This approach faces much criticism ,since it seems to lack the appropriate focus of a country that revolves around its English-speaking culture. Neither of these approaches poses a suitable solution to the issue at hand. Maintaining native language, as we have discussed, is extremely valuable. However, learning English is also an important goal for the future of language-minority students. Thus, the most appropriate bilingual educational approach is one of  careful balance. Native language, although important, should not be the goal, just as English assimilation should not be the central focus. Instead, the goal of bilingual education should be to combine the two former goals and consider them as mutually inclusive. This kind of balanced education is certainly not mainstream, although clearly needed. In Yan’s research regarding parental perceptions of maintaining native language, she found that parents sought after “bilingual schools or those that provided instruction with extra heritage language teaching” (2003, p. 99). Parents of language-minority students recognize the importance of this kind of education and educators and policymakers need to, as well.

The ramifications of native language loss should not be disregarded. Unless bilingual children are actively encouraged and assisted by parents and teachers to maintain their native language, these children will lose their bilingualism. They will not only lose their native fluency and the related benefits, but they will also experience the drawbacks associated with language loss. As the research presented in this article illustrates, there are several specific advantages to maintaining native language. The familial implications reveal that native language loss is detrimental to close relationships with parents and grandparents. Maintaining native language allows for more meaningful communication that can facilitate respect for these relationships as well as heritage culture as a whole. Native language maintenance is also an important factor in the retainment of personal identity. In regard to the social sphere, isolation and a feeling of rejection can occur if native language is not maintained. Additionally, it was found that maintaining native language allows for greater involvement in one’s cultural community. Other social factors included the benefits of bilingualism to the economy as well as the greater scope of job opportunities for bilingual individuals. A variety of studies concluded that there are many cognitive and academic benefits of retaining bilingualism. Due to the many effects of native language loss and the variety of benefits caused by maintaining native language, it can be determined that native language retainment is incredibly important.

Boutakidis, I. P., Chao, R. K., & Rodríguez, J. L. (2011). The role of adolescent’s native language fluency on quality of communication and respect for parents in Chinese and Korean immigrant families. Asian American Journal of Psychology, 2(2), 128–139. doi: 10.1037/a0023606.

Cho, G. (2000). The role of heritage language in social interactions and relationships: Reflections from a language minority group. Bilingual Research Journal, 24(4), 369-384. doi:10.1080/15235882.2000.10162773

Clarkson, P. C. (1992). Language and mathematics: A comparison of bilingual and monolingual students of mathematics. Educational Studies in Mathematics, 23(4), 417.

Fillmore, L. W. (1991). When learning a second language means losing the first. Early Childhood Research Quarterly, 6(3), 323–346. doi: 10.1016/s0885-2006(05)80059-6

Garcia-Vazquez, E., Vazquez, L. A., Lopez, I. C., & Ward, W. (1997). Language proficiency and academic success: Relationships between proficiency in two languages and achievement among Mexican American students. Bilingual Research Journal, 21(4), 395.

Malarz, L. (1998). Bilingual Education: Effective Programming for Language-Minority  Students. Retrieved November 10, 2019, from http://www.ascd.org/publications/curriculum_handbook/413/chapters/Biling... n@_Effective_Programming_for_Language-Minority_Students.aspx .

Mehisto, P., & Marsh, D. (2011). Approaching the economic, cognitive and health benefits of bilingualism: Fuel for CLIL. Linguistic Insights - Studies in Language and Communication, 108, 21-47.

Rodriguez, M. (2019, November 3). Personal interview.

Why is it Important to Maintain the Native Language? (n.d.). Retrieved from https://www.idra.org/resource-center/why-is-it-important-to-maintain-the... language/.

Yan, R. (2003). Parental Perceptions on Maintaining Heritage Languages of CLD Students.

Bilingual Review / La Revista Bilingüe, 27(2), 99-113. Retrieved from http://www.jstor.org/stable/25745785

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Intervention Effects on Language in Children With Autism: A Project AIM Meta-Analysis

Micheal sandbank.

a Special Education Department, The University of Texas at Austin

Kristen Bottema-Beutel

b Lynch School of Education and Human Development, Boston College, MA

Shannon Crowley

Margaret cassidy.

c College of Arts and Sciences, Vanderbilt University, Nashville, TN

Jacob I. Feldman

d Department of Hearing and Speech Sciences, Vanderbilt University, Nashville, TN

Marcos Canihuante

Tiffany woynaroski.

e Department of Hearing and Speech Sciences, Vanderbilt University Medical Center, Vanderbilt Kennedy Center, Vanderbilt Brain Institute, Nashville, TN

This study synthesized effects of interventions on language outcomes of young children (ages 0–8 years) with autism and evaluated the extent to which summary effects varied by intervention, participant, and outcome characteristics.

A subset of effect sizes gathered for a larger meta-analysis (the Autism Intervention Meta-analysis or Project AIM) examining the effects of interventions for young children with autism, which were specific to language outcomes, was analyzed. Robust variance estimation and metaregression were used to calculate summary and moderated effects while controlling for intercorrelation among outcomes within studies.

A total of 221 outcomes were gathered from 60 studies. The summary effect of intervention on language outcomes was small but significant. Summary effects were larger for expressive and composite language outcomes compared to receptive language outcomes. Interventions implemented by clinicians, or by clinicians and caregivers together, had summary effects that were significantly larger than interventions implemented by caregivers alone. Participants' pretreatment language age equivalent scores positively and significantly moderated intervention effects, such that effects were significantly larger on average when samples of children had higher pretreatment language levels. Effects were not moderated by cumulative intervention intensity, intervention type, autism symptomatology, chronological age, or the proximity or boundedness of outcomes. Study quality concerns were apparent for a majority of included outcomes.

Conclusions

We found evidence that intervention can facilitate improvements in language outcomes for young children with autism. Effects were largest for expressive and composite language outcomes, for children with initially higher language abilities, and for interventions implemented by clinicians or by caregivers and clinicians combined. However, quality concerns of included studies and borderline significance of some results temper our conclusions regarding intervention effectiveness and corresponding moderators.

Autism affects an estimated 2 million people in the United States and tens of millions of people worldwide ( Baio, 2012 ). Though the impact of this condition varies substantially, many autistic 1 individuals struggle to acquire adaptive, social, academic, and vocational skills ( American Psychiatric Association, 2000 , 2012 ; Billstedt et al., 2007 ). Long-term outcomes of persons with autism are to some extent impacted by the social communication impairments and restricted, repetitive patterns of behavior, interests, or activities that define the diagnosis ( American Psychiatric Association, 2013 ). However, the single most replicated predictor of long-term outcomes for autistic individuals is the acquisition of language during early childhood (e.g., Billstedt et al., 2007 ; Eisenberg, 1956 ; Gillberg & Steffenburg, 1987 ; Kobayashi et al., 1992 ; Lotter, 1974 ; Rutter et al., 1967 ), making this an especially important focus of intervention efforts.

Children with autism are highly heterogeneous in their ability to understand and use language ( Kjelgaard & Tager-Flusberg, 2001 ; Tager-Flusberg et al., 2005 ). Although some children with autism experience severe language impairments, others display language abilities within the average (or even above average) range relative to their typically developing peers ( Bacon et al., 2019 ; Norrelgen et al., 2015 ; Rose et al., 2016 ; Tager-Flusberg et al., 2005 ). Long-term outcomes of autistic individuals have most commonly been associated with language “production” (i.e., expressive language) acquired during early childhood ( Billstedt et al., 2007 ; Eisenberg, 1956 ; Gillberg & Steffenburg, 1987 ; Kobayashi et al., 1992 ; Lotter, 1974 ; Rutter et al., 1967 ). However, children with autism may also display impairments in language “understanding” (i.e., receptive language) that impact their ability to participate fully in a range of life experiences, as well as their social, academic, and vocational success ( Charman et al., 2003 ; Ellis Weismer et al., 2010 ; Gillum & Camarata, 2004 ; Hudry et al., 2010 ; Luyster et al., 2008 ; Maljaars et al., 2012 ; Volden et al., 2011 ; Woynaroski et al., 2016 ).

Intervention Recommendations

When language is impaired in young children with autism, individualized interventions and family-centered supports are recommended ( American Speech-Language-Hearing Association, n.d. ). These interventions may include home- or clinic-based treatment delivered directly by certified professionals (e.g., certified speech-language pathologists or other trained professionals) and/or caregivers, integration of low-tech or high-tech communicative supports (e.g., Picture Exchange Communication System, speech-generating devices), and structured visual supports (e.g., activity schedules and video modeling of targeted skills). Prior work has shown that intervention (considered broadly) can facilitate improvements in language outcomes for children with autism ( Hampton & Kaiser, 2016 ).

However, it is possible that the extent to which interventions are able to influence child language outcomes varies by characteristics of the intervention, the child, and the specific language outcome being measured. We discuss each of these putative moderators of intervention effects in detail below.

Intervention Characteristics

Various intervention characteristics may moderate effects of treatment on language outcomes. The type of intervention, total amount of intervention delivered (cumulative intensity), and delivery agent (interventionist) are intervention features that may be particularly influential on whether and to what extent interventions are able to influence language outcomes of young children on the autism spectrum.

Intervention type. Multiple intervention approaches have been promoted and used to support language development in children with autism. These approaches vary in terms of their philosophical underpinnings and application. Traditional behavioral interventions, which are adult-led interventions grounded in operant learning theory (e.g., early intensive behavioral intervention [EIBI], Picture Exchange Communication System), are perhaps the most commonly recommended approach for this population. Prior meta-analyses have suggested that behavioral interventions can have positive impacts on language and communication outcomes ( Reichow et al., 2012 , 2018 ), but recent work has suggested that these effects may be inflated by the inclusion of outcomes from low-quality investigations ( Sandbank et al., 2019 ). In contrast, developmental approaches (e.g., DIR/Floortime, Hanen models) are rooted in constructivist theories that posit that children must be active participants in their own learning experiences. Prior work has shown that developmental approaches can facilitate improvements in language ( Casenhiser et al., 2013 ). However, recent meta-analytic work has suggested that the summary effect of developmental interventions on language outcomes is not significantly different from zero ( Sandbank et al., 2019 ). Naturalistic Developmental Behavioral Interventions (NDBIs; Schreibman et al., 2015 ) employ strategies from both of the aforementioned approaches and are frequently deployed to target improvements in language in young children with autism. These interventions blend behavioral and constructivist techniques to target language milestones in the context of natural interactions between children and caregivers. Intervention targets are chosen using the developmental sequence established in research on typical child development. Prior meta-analytic work has suggested that the summary effect of NDBIs on language outcomes is positive and significantly different from zero, but this estimate may be heavily influenced by outcomes from investigations wherein assessors were aware of group assignment ( Sandbank et al., 2019 ). Sensory-based interventions, which target improvements in sensory processing and related skills by modulating auditory, visual, tactile, and/or other sensory input, have aimed to improve language outcomes in children with autism. Recent meta-analytic and systematic reviews of sensory-based interventions, however, have suggested that sensory-based approaches may have limited effectiveness for improving language and related outcomes (e.g., Barton et al., 2015 ; Sandbank et al., 2019 ). TEACCH (Treatment and Education of Autistic and Related Communication Handicapped Children; Mesibov & Shea, 2010 ) is a classroom-based intervention that heavily incorporates visual cues and structured work systems. Although TEACCH is one of the most widely used interventions for children with autism, relatively few group design studies have documented the effects of TEACCH on language outcomes ( Virues-Ortega et al., 2013 ). Finally, technology-based interventions are those that use technology (e.g., computers, video games, robots) as the primary medium of instruction and support, thereby capitalizing on autistic persons' reported special interest in technology, which provides predictable formatting and user-controlled pace of information delivery ( Knight et al., 2013 ). Although studies have shown that the integration of technology into other intervention approaches can enhance intervention effects on language acquisition ( Kasari et al., 2014 ), recent meta-analytic works suggest there are few studies of interventions “entirely mediated” through technology that document their effects on language outcomes ( Sandbank et al., 2019 ). If intervention type significantly moderates language outcomes, it may provide support for the choice of specific interventions over others for targeting receptive and expressive language in children with autism.

Cumulative intensity. The cumulative amount of intervention provided may also moderate intervention effects on language of children on the autism spectrum. In general, greater amounts of intervention are thought to yield greater effects for children with autism, and recommendations for high numbers of intervention hours per week (e.g., > 25 hr per week; Odom et al., 2010 ) are rooted in this claim. In some cases, evidence has seemingly supported this assertion. For example, initial studies of EIBI suggested that children receiving the most hours of intervention made the greatest gains ( Lovaas, 1987 ; Reichow & Wolery, 2009 ). However, in recent meta-analytic work examining intervention effects on spoken language outcomes for children with autism, Hampton and Kaiser (2016) failed to find evidence that treatment intensity moderated effects. At least one study suggested that the effect of treatment intensity on child outcomes may vary as a function of autism symptomatology. In their recent randomized comparison of different intervention intensities, Yoder and colleagues found that increased intensity did improve intervention effects, but only for participants with lower Autism Diagnostic Observation Schedule (ADOS)–calibrated severity scores ( P. J. Yoder et al., 2018 , 2019 ). If intensity does significantly moderate effects of intervention on language outcomes, such that more intense intervention yields larger effects, this will add to evidence supporting calls for increased access to high-intensity intervention for at least some children with autism. However, it is possible that a higher number of intervention hours does not yield better outcomes, or that this association is quadratic, where modest increases in intervention dosage (e.g., 5–10 hr per week) yield substantial improvements compared to lower and higher dosages.

Interventionist. Intervention effects may also vary depending on whether the intervention is delivered by a clinician, a caregiver, or a combination of both. Clinicians are trained professionals who may have special knowledge of ways to elicit communication, sustain interaction, and increase targeted opportunities for language learning. Alternatively, caregivers may be more ideal agents of change because they are familiar interaction partners, spend large amounts of time with their children, and have meaningful relationships with them. Thus, interventions that are delivered primarily through caregivers may be delivered throughout the day and across contexts and may therefore facilitate greater and more generalized changes. Moreover, when caregivers and clinicians work together to deliver interventions, they may capitalize on the expert knowledge of the clinician and the familiarity and consistent presence of the caregiver, thereby facilitating larger effects than interventions delivered by clinicians or caregivers alone. Prior meta-analytic work suggests this may be the case for interventions targeting language outcomes. Hampton and Kaiser (2016) found that interventions delivered by both parents and clinicians yielded larger treatment effects on spoken language outcomes than interventions delivered by parents or clinicians alone.

Participant Characteristics

Intervention effects on language outcomes may also vary according to mean-level characteristics of the participants enrolled in the study, such as chronological age, autism symptomatology, and entry-level language ability.

Chronological age. A common recommendation for children with autism is that intervention begins as early as possible, with some advocating for intervention to begin even prior to diagnosis (e.g., Beaudoin et al., 2014 ; Boyd et al., 2010 ; Landa, 2018 ; Reichow, 2012 ). This recommendation stems from neuroscientific theories of neuronal plasticity, which suggest brain architecture is most malleable at younger ages, potentially allowing for greater developmental improvements associated with intervention ( Dawson, 2008 ). Additionally, early studies of EIBI have suggested treatment gains were greatest for participants that began intervention at an early age ( Lovaas, 1987 ). However, age was not associated with treatment gains in subsequent studies of EIBI ( Eldevik et al., 2012 ; Hayward et al., 2009 ; Reichow & Wolery, 2009 ), and a recent meta-analysis examining the effects of intervention (considered broadly) on spoken language outcomes in children with autism found that age did not significantly moderate effects of intervention on outcomes ( Hampton & Kaiser, 2016 ). In contrast, a recent meta-analysis of intervention effects on an outcome developmentally related to language, social communication, suggested that age did moderate intervention effects ( Fuller & Kaiser, 2019 ). If age does significantly moderate treatment effects on outcomes, such that intervention effects are larger for younger children compared to older children, it will bolster the evidence prioritizing access to earlier diagnostic and intervention services.

Autism symptomatology. The heterogeneous nature of autism may also impact the effects of intervention on language outcomes. Prior work has shown that children with fewer or less severe impairments related to the core features of autism demonstrated greater improvements in language while receiving intervention ( Ben-Itzchak & Zachor, 2007 ; Gordon et al., 2011 ). However, other rigorous tests of intervention efficacy have failed to document significant associations between pretreatment autism symptomatology scores and change in language outcomes ( Rogers et al., 2019 ). It is also possible that the association between autism symptomatology and intervention effects on language is further complicated by intervention intensity. P. J. Yoder et al. (2018 , 2019) compared effects of Applied Behavior Analysis therapy and the Early Start Denver Model as delivered at relatively low and high intensities and found that, across intervention types, children with lower autism symptomatology scores benefited more from high-intensity intervention compared to those with higher autism symptomatology scores. Thus, intervention may have differential effects depending on autism severity, and a greater understanding of this association could add to evidence supporting the development of more individualized intervention protocols.

Language ability. Several studies have shown that initial language ability predicts eventual language outcomes in both developmental and intervention studies of children with autism, where children with higher initial scores on measures of language ability exhibit steeper developmental slopes and benefit more from interventions targeting language ( Paul et al., 2013 ; Vivanti, Prior, et al., 2014 ; P. Yoder et al., 2015 ). These findings suggest that stronger foundational language skills may allow children with autism to “tune in” and learn more from linguistic interactions that occur in developmental and intervention contexts ( Woynaroski et al., 2016 ). Given that initial language age equivalents in months are commonly reported in participant demographics and that this scale is comparable across studies even when different language measures are used, it is possible to use language age equivalency scores and meta-analytic strategies to determine if there is a significant interaction between pretreatment language ability and intervention effects on language outcomes. As several individual studies have documented a positive association between language ability and intervention gains, it is likely that average participant language age equivalency will moderate intervention effects on language, such that larger effects are observed for participant samples with greater average language age equivalence scores at study entry.

Outcome Characteristics

Effects of intervention on language in young children with autism may further vary according to a number of different characteristics of the outcome measures employed.

Language outcome subtype. Language encompasses both the comprehension and production of a spoken, written, or other communicative symbol system (e.g., American Sign Language). Language outcomes can therefore be divided into those that index receptive language, expressive language, or both receptive and expressive skills (i.e., language composite). Multiple studies have suggested that children with autism may display disproportionate delays or deficits in receptive relative to expressive language (e.g., Barbaro & Dissanayake, 2012 ; Hudry et al., 2014 ; McDaniel et al., 2018 ; Woynaroski et al., 2016 ).We hypothesized that receptive language may also be less responsive to intervention than expressive language and that effect sizes indexing intervention effects on receptive language would thus be significantly smaller than those for expressive language outcomes in this clinical population. We expected that summary effects for composite language, which index intervention effects on receptive and expressive language together, would likely be smaller than summary effect sizes for expressive language alone, but larger than summary effect sizes for receptive language.

Proximity. Intervention outcomes that are directly taught or prompted by the intervention are considered “proximal,” whereas outcomes that are broader and/or developmentally beyond what was directly taught or prompted are considered “distal.” In terms of language outcomes, children's production of particular linguistic forms that were the focus of the intervention (e.g., saying “hello” or “goodbye”) could be considered proximal, whereas performance on a standardized language assessment could be considered distal. Distal effects are more desirable because they suggest that the intervention has tapped into a developmental process, making continued child growth after the intervention has stopped more likely (e.g., Kasari et al., 2008 ). However, previous research syntheses have shown that interventions are more likely to show proximal effects ( Yoder et al., 2013 ) and that proximal effects tend to be larger than distal effects in young children with autism ( Sandbank et al., 2019 ).

Boundedness. Some intervention outcomes are measured in contexts that are very similar to the intervention itself, including the materials used, the procedures for eliciting the outcome (e.g., prompting and reinforcement), the interaction partner, and the setting. If this is the case, the outcome can be considered potentially “context bound.” In contrast, outcomes that are measured in contexts that are very different from the intervention on the dimensions listed above are considered more highly “generalized.” In most cases, generalized outcomes are more favorable, as they suggest that the outcome has become an established part of the child's repertoire and is not dependent on particular aspects of the intervention in order to occur. Similar to the aforementioned findings for proximal effects, however, research has shown that interventions are more likely to produce effects on context-bound outcomes ( Yoder et al., 2013 ) and that effect sizes tend to be larger for context bound as compared to generalized outcomes in young children on the autism spectrum ( Sandbank et al., 2019 ).

The primary purpose of the current investigation was to estimate the summary effect of intervention (considered broadly) on language outcomes as a whole and to evaluate the extent to which intervention effects vary by language outcome subtype (e.g., receptive, expressive, composite) for children with autism during the early childhood period (i.e., between birth and 8 years of age). Although prior meta-analytic work has estimated the summary effects of specific intervention approaches (e.g., behavioral, developmental, NDBI, parent-implemented treatments) on language outcomes ( Roberts & Kaiser, 2011 ; Sandbank et al., 2019 ), as well as the summary effect of intervention on spoken language outcomes ( Hampton & Kaiser, 2016 ), no prior meta-analysis has estimated the summary effects of intervention across approaches and across broader language outcomes in young children with autism. The secondary purpose of this investigation was to examine the degree to which intervention effects on language outcomes were moderated by various characteristics of the intervention approach, participant sample, and outcomes measured. Our research questions were as follows:

• Are the summary effects of intervention on language outcomes in children with autism, considered broadly, and by subtype of receptive, expressive, and composite language outcomes, significant and positive?
• Are the effects of intervention on language outcomes moderated by any one of nine putative moderators (intervention type, cumulative intensity, interventionist, participant age, autism symptomatology, entry-level language ability, language subtype, proximity, and boundedness)?

Given prior evidence and theory, we hypothesized that summary effects would be significantly larger for interventions with greater cumulative intensity, as well as for those interventions that were delivered by a combination of parents and clinicians as compared to either parents or clinicians alone. Second, we hypothesized that summary effects would be larger for participant samples with lower mean age and autism symptomatology scores, as well as with higher entry-level language ability. Finally, we hypothesized that intervention effects would be smaller for receptive relative to expressive and composite language outcomes and that effects for proximal and context-bound outcomes would be significantly larger than effects for distal and generalized outcomes.

The current review article is part of a larger systematic review and quantitative synthesis of all nonpharmacological intervention studies of young children with autism, referred to as the Autism Intervention Meta-analysis or Project AIM ( Sandbank et al., 2019 ). Relevant search and coding procedures are reiterated here briefly.

A wide search strategy was used for the initial project ( Sandbank et al., 2019 ). Nine online databases (Academic Search Complete, CINAHL Plus with Full Text, Education Source, Educational Administration Abstracts, ERIC, MEDLINE, PsycINFO, Psychology and Behavioral Sciences Collection, and SocINDEX with Full Text) were searched with the following search terms: autis*, ASD, PDD, Aspergers, intervention, therapy, teach*, treat*, program, package, assign*, control group, BAU, “wait list,” RCT, random*, quasi, “treatment group,” “intervention group,” “group design,” and trial. Synonymous terms were joined with the boolean operator “OR,” and groups of terms across categories were joined with the boolean operator “AND.” The final electronic search was completed on November 16, 2017. To gather unpublished data, we also searched the National Database for Autism Research, the National Institutes of Health Matchmaker, and the Institute of Education Sciences to identify researchers who had been awarded federal grants to study autism interventions. Of 106 researchers identified through this search, 90 were contacted by e-mail with a request to share eligible outcome data. The contact information for the remaining investigators could not be found. The initial search yielded 12,933 results and captured a broad scope of the intervention literature (i.e., reporting on effects of all intervention types on all outcome types for children with autism across early childhood). Studies were eligible for inclusion in the current meta-analysis if they were experimental or quasi-experimental group design studies examining the effect of an intervention on language outcomes for young children (aged 0–8 years) with autism, regardless of whether or not the intervention was explicitly reported to directly target language. A total of 60 studies met the inclusion criteria for this subgroup analysis of the larger Project AIM data.

Coding Procedures

After the search and screening process, studies were coded for study, participant, intervention, and outcome characteristics. The coding manual used by primary and reliability coders is available upon request from the first author, and data have been deposited in the Open Science Framework: https://osf.io/ha76c/?view_only=55f42fb1e14a4af3a16d5885b301a8b5 .

Studies were coded to include pre-intervention chronological age in months, autism symptomatology, and entry-level language ability of study samples. Autism symptomatology scores were taken from eligible measures when reported. These included all versions of the ADOS, the Autism Diagnostic Interview–Revised, and the Childhood Autism Rating Scale. Scores were then categorized as either “moderate” or “high” using published guidelines for score interpretation ( Gotham et al., 2009 ; Hus et al., 2014 ; Luyster et al., 2009 ; Schopler et al., 1988 ) or guidance from individuals with expertise in the quantification of autism symptomatology from the aforementioned standardized measures (K. Gotham, personal communication, November 8, 2018). When autism symptomatology scores were not reported or when reported scores could not be categorized based on published guidelines, autism symptomatology was coded as missing. When reported, initial language ages in months were also extracted from the study. Expressive language age was prioritized over receptive language age (as we suspected, expressive language age would be more commonly reported), but receptive and total language age were extracted if expressive language age was not reported.

Interventions were coded as belonging to one of nine categories. These included animal-assisted therapy, behavioral, developmental, NDBI, cognitive behavior therapy, sensory based, technology based, and TEACCH. Interventions that were not captured by these categories were coded as “other.” Explicit criteria that were used to categorize intervention approaches are detailed in the main project report ( Sandbank et al., 2019 ). The cumulative intensity of intervention in hours participants received was also coded when estimable from study details. For interventions that incorporated parent training while child participants were present, training time was included in intensity calculations. Parent training hours were excluded from intensity calculations if children were not present during this time. Lastly, the person/s who delivered the intervention to the participants was coded as either caregivers, clinicians, educators, peers, or technology (e.g., when the intervention was completely mediated through a DVD or computer). If a substantial portion (> 40%) of intervention hours was delivered by individuals from two of the above categories, either working together at the same time or separately, then the type of interventionist was coded as “combination.”

The outcomes analyzed in this review article are those that index the postintervention language ability of the participants in treatment conditions relative to contrast/control groups. Language outcomes were further classified as indexing receptive, expressive, or composite language ability. As outlined in the study of Sandbank et al. (2019) , each outcome was also coded for quality indicators, including the risk of selection bias, attrition bias, detection bias, correlated measurement error related to parent/teacher training, and reliance on caregiver report. Selection bias was coded as high for outcomes taken from studies that were quasi-experimental or that featured insufficient randomization procedures (as opposed to randomized controlled trials [RCTs]). Attrition bias was coded as high for outcomes for which data were missing for more than 20% of participants, where intent-to-treat analysis was not employed. Detection bias was coded as high for outcomes collected by assessors who were aware of group assignment. The potential threat of correlated measurement error related to parent/teacher training was coded as high when parents or teachers participated in the study as both interventionists and assessors (see Sandbank et al., 2019 , for an expanded rationale regarding this quality indicator).

Outcomes were also coded in regard to their proximity to intervention targets and their boundedness to intervention contexts. Outcomes were coded as proximal if they indexed skills that were directly taught or prompted during intervention. They were coded as distal if they indexed skills (a) that were not directly targeted in the intervention and/or (b) that were measured via developmentally scaled assessments, which are designed to capture developmental progress across a given domain rather than acquisition of specific skills. Outcomes were coded as context bound if they were measured in contexts that were the same or similar to the intervention context, potentially context bound if they were measured via a report from a parent or teacher that delivered the intervention (as these assessments do not indicate the extent to which children displayed language gains with partners who were not part of the intervention), and generalized if they were measured in a context that differed from that of the intervention context on several dimensions (e.g., interaction partner, interaction style, materials, setting).

Effect Size Information

Unadjusted postintervention means, standard deviations, and n s were extracted for treatment and contrast groups. This information was used to calculate the standardized mean difference reflecting the difference between groups after intervention. Standardized mean differences were then converted to Hedge's g to correct for small sample sizes (applicable to sample sizes < 50), which was the effect size metric used for analyses. Effect sizes were transformed as necessary so that higher effects consistently reflected outcomes that favored the intervention group.

Summary Effect Estimation

The summary effects of intervention were estimated for all language outcomes and for subgroups of receptive, expressive, and composite outcomes, using intercept-only robust variance estimation (RVE; Hedges et al., 2010 ) metaregression models. A primary assumption of meta-analysis is that effects are independent, but this assumption is violated when multiple effects are extracted from individual studies. To address this violation, we used RVE to account for the within-study dependence of effects. RVE estimates the covariance structure of metaregression coefficients by assuming a common correlation between all effect sizes (ρ). RVE is a useful approach because it does not require assumptions about the distribution of effect sizes or their weighting scheme ( Tipton, 2015 ). All RVE analyses were conducted with ρ set at 0.8 and then followed by sensitivity analyses to ensure that changing the value of ρ did not substantially change summary effect estimates. We also applied the small sample adjustments proposed by Tipton (2015) , which correct degrees of freedom to allow the researcher to evaluate whether the RVE approach was suitable for the given analysis. Results with fewer than four degrees of freedom should not be trusted ( Tanner-Smith & Tipton, 2014 ).

Moderator Analyses

Moderator analyses were conducted using metaregression, which applies the logic of regression to meta-analysis, where effect sizes are treated as the dependent variable in a regression model, and study-level covariates (i.e., moderators) are entered as predictors. Prior to building metaregression models, we first examined putative associations between continuous pretreatment predictors and intervention outcomes using visual inspection of bubble plots to ensure that linear models were sufficient and that the addition of quadratic terms was not needed. For categorical predictors, metaregression analyses were used to determine the intervention effect in each subgroup in reference to nominated reference subgroups. Metaregression analyses were conducted on the following putative moderators: intervention type, intervention intensity, interventionist, chronological age, autism symptomatology, language age, and language outcome subtype. An exploratory post hoc metaregression analysis was also carried out to test a potential interaction between autism spectrum disorder symptomatology and intervention intensity that was suggested by the results of one recent RCT testing the efficacy of treatments delivered at relatively high versus low intensity in young children with autism ( P. J. Yoder et al., 2018 ). Summary effect estimates and metaregression analyses were conducted with the R package Robumeta ( Fisher et al., 2017 ), and plots were created with the R package Metafor ( Viechtbauer, 2010 ).

Publication Bias

Finally, we conducted tests of publication bias, because a publication process, which gives preferential treatment to studies that document significant intervention effects over studies with null results, can yield a set of published studies that are largely unrepresentative of the complete body of evidence and lead to an overestimation of summary effects. Publication bias potentially threatens all meta-analytic results; therefore, it is essential that meta-analyses in speech, language, and hearing research include multiple tests designed to evaluate the potential influence of publication bias ( Chow, 2018 ). Common methods for this purpose involve testing the association between effect sizes and their precision, because effect estimates from small studies should vary most (due to random error), whereas effect estimates from large studies should vary least. To this end, we visually inspected funnel plots of effect sizes plotted against their standard errors and tested the asymmetry of these plots using an Egger's regression test ( Egger et al., 1997 ). The results of these tests provide quantitative information that allow us to evaluate the likelihood that summary effects were influenced by publication bias.

Reliability

All reliability calculations were completed using the irr package ( Gamer et al., 2012 ) in R studio ( R Core Team, 2017 ). Coding reliability was calculated for 100% of studies included in the larger meta-analysis, using two-way random, single measures, absolute intraclass correlations for continuous variables (ICC[A, 1]; McGraw & Wong, 1996 ) and unweighted kappa coefficients for categorical variables ( J. Cohen, 1960 ). Reliability was high. Average kappa values across categorical variables included in the current review article was .82 (range: .73–.91), and the average intraclass correlation across continuous variables was .88 (range: .81–.96).

Descriptives of Included Study Samples and Outcomes

A total of 221 outcomes gathered from 60 studies featuring a total of 2,908 participants were included in the current analyses. Of included outcomes, 130 were coded as expressive, 79 were coded as receptive, and 12 were coded as tapping both expressive and receptive language (composite language). The mean number of language outcomes reported per study was 3.68 (min = 1, max = 14). On average, participants were 47.5 months old ( SD = 16.45). The average percentage of male participants across samples was 82.7%. Average participant language age was 20.19 months ( SD = 7.52) for studies in which it was reported. See Table 1 for included studies and their corresponding participant and intervention characteristics.

Characteristics of participant samples and interventions in included studies.

Note.  Studies are listed twice when more than one intervention group was evaluated in reference to a comparison. Em dashes indicate information was not reported. CA = chronological age; LA = language age equivalent; TEACCH = Treatment and Education of Autistic and related Communication Handicapped Children; LEAP = Learning Experiences—An Alternative Program for Preschoolers and Parents; NDBI = Naturalistic Developmental Behavioral Intervention.

Outcome Quality

Figure 1 reflects outcome-level quality indicators. Out of 221 outcomes, 135 (61%) indexed intervention effects in RCTs, 25 (11%) were coded as having high risk of attrition bias due to a large percentage of missing outcome data, and 126 (51%) were coded as having high detection bias due to reliance on assessors that were aware of group assignment. The threat of correlated measurement error related to parent/teacher training was marked as high for 71 outcomes (32%), where parents or teachers participated both as interventionists and assessors. A total of 59 outcomes (26%) were derived from caregiver report. We coded 21 outcomes (9.5%) as proximal to intervention targets. Finally, 31 outcomes (14%) were coded as bound to the context of intervention, 47 outcomes (21%) were coded as potentially context bound, and 143 (65%) were coded as generalized.

Quality indicators for studies of interventions with language outcomes. RCT = randomized controlled trial.

Summary Effects of Intervention on Language Outcomes

Forest plots of included effect sizes and summary effects for receptive, expressive, and composite language outcomes are presented in Figures 2 , ​ ,3, 3 , and ​ and4. 4 . Across all language outcomes and intervention types, the RVE summary estimate with small sample corrections was 0.184 (95% CI [0.075, 0.292], p = .001). Metrics for quantifying the dispersion of effect sizes suggest there was substantial heterogeneity ( I 2 = 58.6%, τ 2 = 0.123, τ = 0.351). For expressive language outcomes, the RVE summary estimate was 0.18 (95% CI [0.077, 0.283], p = .001). For receptive language outcomes, the RVE summary estimate was 0.135 (95% CI [0.000, 0.269], p = .05). For composite language outcomes, the RVE summary estimate was 0.284 (95% CI [−0.0465, 0.614], p = .084). Following benchmarks for main effects proposed by Cohen (1988) , where effect sizes of 0.2, 0.5, and 0.8 are considered small, moderate, and large, respectively, these effects are relatively small. However, it should be noted that summary effects for education interventions tend to fall within a range of 0.20–0.30 ( Hill et al., 2008 ), so the magnitude of these effect sizes might be more appropriately interpreted as moderate.

Forest plot of Hedge's g effect sizes reflecting intervention effects on receptive language outcomes. RVE = robust variance estimation.

Forest plot of Hedge's g effect sizes reflecting intervention effects on expressive language outcomes. RVE = robust variance estimation.

Forest plot of Hedge's g effect sizes reflecting intervention effects on composite language outcomes.

Table 2 provides descriptive information for putative moderators. Detailed results of tested metaregression models are presented in Table 3 with both uncorrected and corrected p values ( Benjamini & Hochberg, 1995 ). Benchmarks for small, moderate, and large moderator effects are 0.10, 0.20, and 0.40, respectively ( Tipton, 2015 ), but these benchmarks are meant to complement Cohen's benchmarks for main effects. If we consider a main effect of 0.25 to be moderate, then the corresponding moderate metaregression coefficient estimate would be 0.13 (e.g., half of the main effect benchmark). Significant intercept estimates should not be interpreted as evidence that effects were significantly moderated by tested predictors. Rather, this suggests that the summary effect of intervention is significantly different from zero when the value of the predictor is 0 (for continuous moderators), or that the summary effect of the reference category is significantly different from 0 (for categorical moderators).

Features of continuous and categorical moderators.

Note.  NDBI = Naturalistic Developmental Behavioral Intervention; TEACCH = Treatment and Education of Autistic and Related Communication Handicapped Children.

Results of metaregression analyses.

Note. p adj = p values after utilizing a Benjamini–Hochberg correction ( Benjamini & Hochberg, 1995 ); NDBI = Naturalistic Developmental Behavioral Intervention; TEACCH = Treatment and Education of Autistic and Related Communication Handicapped Children.

Results from the metaregression models that included intervention variables indicated that effect sizes were not moderated by intervention type (see Table 3 ) or by cumulative intervention intensity in hours. However, interventionist was a significant moderator of effect sizes, when alpha was nominally set at .05. Results from the metaregression model that included the categorical variable of interventionist, with caregiver as the reference category, indicated that intervention effects were significantly larger for interventions implemented by clinicians compared to caregivers alone ( B = 0.33, p = .044) and marginally larger for those implemented by a combination of interventionists (e.g., caregivers and clinicians working together, B = 0.26, p = .058) compared to caregivers alone. However, when p values were corrected to account for multiple comparisons, they did not pass the significance threshold. Effect sizes for interventions implemented by other interventionist types (e.g., educator, computer-mediated instruction) did not significantly differ from caregiver-implemented intervention effects. Figure 5 displays summary effects and confidence intervals by interventionist type.

Robust variance estimation summary effect estimates and corresponding confidence intervals for language outcomes according to interventionist type. *Effect sizes for which degrees of freedom were too few to permit confidence in the accuracy of the estimate.

Results from metaregression models that included participant characteristics suggested that chronological age at intervention onset did not moderate intervention effect sizes ( B = 0.03, p = .641). Autism symptomatology categorizations also did not moderate intervention effects: Significantly different effects were not observed for participant samples rated as “high symptomatology” compared to samples for which autism symptomatology was categorized as “moderate” ( B = 0.09, p = .639). Language age in months did significantly moderate intervention effects, such that higher mean language ages were associated with larger intervention effects ( B = 0.25, p = .010). This result was significant even after correcting for multiple comparisons. Figure 6 displays a bubble plot of effect sizes plotted against participant mean language age equivalents.

Bubble plot of Hedge's g effect sizes against mean participant language age equivalency scores in months. The dotted line reflects a summary effect of zero. The black line reflects the linear model predicting intervention effects from mean language age, controlling for intercorrelation among outcomes within study using robust variance estimation. Bubble size is proportional to the outcome model weights.

Results from the metaregression model that included outcome type as a categorical variable, with expressive outcomes as the reference category, indicated that intervention effects were significantly smaller for receptive outcomes compared to expressive outcomes ( B = −0.13, p = .047), though this effect was not significant after correcting for multiple comparisons. Effect sizes for composite language outcomes did not differ significantly from expressive outcomes ( B = 0.16, p = .277). Figures 2 , ​ ,3, 3 , and ​ and4 4 display RVE summary estimates for receptive, expressive, and composite language outcomes, respectively. Effect sizes did not significantly vary as a function of outcome boundedness or distality. Compared to outcomes coded as context bound, effect sizes were smaller but not significantly different for outcomes coded as potentially context bound ( B = −0.28, p = .161), as well as those coded as generalized ( B = −0.25, p = .216). Effect sizes for outcomes coded as proximal were, on average, larger than those coded as distal, but this difference was not statistically significant ( B = 0.29, p = .344).

Exploratory Post Hoc Analysis

Results from the exploratory metaregression model that tested a putative interaction between intervention intensity and autism symptomatology categorization indicated the interaction term was not significant ( B = −0.176, p = .5).

Publication Bias Analyses

Results of the Egger's regression test of funnel plot asymmetry for the full model (i.e., across all language outcomes and intervention types) was significant, z = 2.52, p = .012, indicating evidence of publication or small sample bias. Follow-up analyses indicated that this effect was driven by the expressive language outcomes model, z = 4.54, p < .001. Neither the receptive language outcomes model, z = 1.38, p = .17, nor the composite language outcomes model, z = 0.66, p = .51, showed evidence of publication or small sample bias. Funnel plots of effect sizes plotted against their standard errors, across all outcomes, according to outcome type are presented in Figure 7 .

Funnel plots of Hedge's g effect sizes against standard errors.

The purpose of this meta-analysis was to determine whether existing interventions significantly improve language of young children with autism, considered broadly and more specifically according to subtype of receptive, expressive, and composite language outcome. As well, we sought to determine whether intervention effects were moderated by various aspects of the participant samples, interventions, and outcomes. These results suggest that interventions on average yield small-to-moderate, significant improvements in language outcomes for children with autism in early childhood. Summary effect estimates of intervention are significantly smaller for receptive language outcomes relative to summary effects for expressive language outcomes for children with autism during this developmental period. In addition to type of language outcome, other significant moderators of intervention effects included the interventionist/s and the entry-level language ability of participants. However, only entry-level language ability was a significant moderator after correcting for multiple comparisons. We failed to find evidence that intervention type, cumulative intervention intensity, chronological age of participants, participant autism symptomatology, boundedness of the outcome, and distality of the outcome significantly moderated intervention effects, though it is possible that study-level factors, measurement imprecision, and a limited number of studies prevented us from uncovering a potentially true association between any of these putative moderators and intervention effects. Analysis of study quality indicates that there are several areas in which study designs will need to be improved in future research in order to draw strong conclusions about the effects of interventions on language outcomes of young children with autism. Below, we discuss findings for each moderator explored in this study and corresponding recommendations for primary intervention research.

Intervention Characteristics and Intervention Effects

Intervention type.

Though our prior work suggested that intervention effects may vary substantially by intervention type for some outcomes of interest in young children on the autism spectrum ( Sandbank et al., 2019 ), we did not find evidence in the current review article to suggest that summary effects across language outcomes significantly vary according to intervention type. In other words, though summary effect estimates differ to some degree by intervention type, the confidence intervals surrounding each of these estimates are wide (suggesting high variability of effects within intervention type) and overlap with one another. This should not be construed as evidence that all intervention approaches targeting language outcomes in this population are equivalent. The philosophical underpinnings and techniques employed in these interventions differ extensively, and these differences likely matter. However, at least one recent randomized comparison of this nature documented nonsignificantly different improvements in groups assigned to receive distinct intervention approaches, adding to the evidence that intervention approaches motivated by opposing philosophies may be associated with similar effect sizes ( P. J. Yoder et al., 2018 , 2019 ).

It is also possible that effects of intervention type vary according to participant (sample) characteristics. Such interactions have been observed in prior clinical trials of interventions targeting language and communication outcomes in young children with autism (e.g., P. Yoder & Stone, 2006 ). We were unable to test this question in the present synthesis, as the limited number of studies and missing data prohibited the addition of Intervention Type × Participant Characteristic product terms as predictors in models summarizing effects of interventions on language outcomes. Future RCTs that permit the testing of differential effects of high-quality instantiations of different intervention approaches according to child characteristics are thus necessary to elucidate whether certain interventions are more optimal choices compared to others for facilitating improvements in language for specific subgroups of children on the autism spectrum and thereby advance us toward more personalized treatment planning for this clinical population.

We did not find evidence that greater intervention intensities were significantly associated with greater gains in language. This finding is consistent with the findings of prior meta-analyses of intervention effects on language ( Hampton & Kaiser, 2016 ; Virues-Ortega, 2010 ) and related outcomes ( Fuller & Kaiser, 2019 ) in children with autism, though it departs from frequent assertions that more intervention will yield more improvement. It is possible that a true association between cumulative intensity and intervention effects is complicated by aspects of treatment intensity other than the cumulative number of hours of intervention, such as within-session dosage (i.e., number of teaching episodes effectively delivered per intervention session), dose frequency (i.e., number of intervention sessions per week), and/or the total duration (i.e., total number of weeks over which intervention is provided), as well as participant characteristics, such as autism symptomatology. Though some of the aforementioned, more fine-grained aspects of intervention intensity could not be analyzed because they were not extracted in the original coding system for the larger meta-analysis (and notably often were not reported in extant studies), we were able to conduct exploratory post hoc analyses to examine whether autism symptomatology interacted with cumulative intensity to predict intervention effects. The results of this analysis are discussed below.

If future meta-analyses are to clarify the impact of intervention intensity on outcomes, it will be important for investigators to report a range of metrics that comprehensively describe intervention dose, dose frequency, and duration ( Warren et al., 2007 ), as well as participant characteristics that may predict a differential response to more versus less intense intervention, such as autism symptomatology. Future randomized comparisons of low- and high-intensity versions of single interventions can also increase our understanding of the overall impact of intervention intensity and provide a framework for moderator analyses that elucidate the extent to which this impact differs by participant characteristics. Finally, sequential multi-assignment randomized trial design studies may be particularly useful for determining the extent to which increases in intervention intensity alter the trajectory of change for children who may initially show no or minimal gains during intervention. Until then, it will be important for professionals recommending high-intensity intervention to weigh the potential positive impact of increased intervention with the potential negative impact of stress that high-intensity interventions can place on young children and their families.

Interventionist/s

We did find evidence that the implementer of the intervention (clinician, caregiver, educator, combination, etc.) is a key factor that impacts the magnitude of intervention effects on language outcomes. Compared to interventions implemented by caregivers alone, for which the summary effect was small and not significantly different from zero, interventions delivered primarily by clinicians and interventions delivered by a combination of interventionists (i.e., caregivers and clinicians working together) yielded significantly larger effects, though these results for moderated effects notably no longer exceeded the threshold for statistical significance after correcting for multiple comparisons. The largest summary effect estimate was observed for interventions delivered by clinicians only. This result contrasts with the findings of Hampton and Kaiser (2016) , who found that the summary effect of clinician-led interventions on spoken language outcomes was very small and not significantly different from zero. The inclusion of approximately 200 more effect sizes in our analysis of this question likely explains the apparent discrepancy in findings across syntheses.

The summary effects for interventions delivered primarily by educators and for interventions mediated entirely through technology were also small and not significantly different from the summary effect for treatments delivered by caregivers. These results suggest that, though caregivers and educators are primary figures in young children's lives, their ability to directly effect change in language outcomes may be limited. Clinicians who have specialized training in language development and treatment (e.g., speech language pathologists) are likely the most equipped to facilitate language development, either by working directly with children or working with caregivers and educators to provide intervention across the natural environments that children encounter each day. However, we note that there may be language outcomes that were not measured in this analysis (e.g., those that tap more social and/or pragmatic features of language) that may be more influenced by caregivers than by clinicians.

Participant Characteristics and Intervention Effects

Chronological age.

Chronological age did not emerge as a significant moderator of intervention effects on language outcomes in this meta-analysis. This finding is consistent with prior meta-analytic work in this area ( Hampton & Kaiser, 2016 ) but may also be due to the restricted age range defined for eligible studies (i.e., aged 0–8 years). Our results should not be construed as evidence that early intervention is not important for children with autism. Multiple studies included in this meta-analysis demonstrated that intervention can have positive effects on language outcomes for very young children with autism, and early gains in language may produce cascading effects on development in related domains as children age. Thus, early access to diagnosis and intervention continue to be necessary for children with autism. Rather, we see our results as adding to mounting evidence that advancing age does not place a limit on the amount of change that can be achieved with intervention. Rather, children can benefit from language interventions throughout early childhood (i.e., birth to 8 years).

Autism Symptomatology

We additionally did not find evidence that autism symptomatology moderated intervention effects on language outcomes for children on the autism spectrum. A number of challenges may have limited our ability to detect a true association between autism symptomatology and intervention effects, and these are detailed in the Limitations section. The relatively recent creation of the ADOS calibrated severity score algorithms ( Gotham et al., 2009 ), which provide a continuous measure of symptomatology that is comparable across age groups and ADOS modules, could facilitate direct comparison of separate participant samples going forward. Thus, increased reporting of calibrated severity scores to quantify autism symptomatology could allow future meta-analysts to better estimate the true impact of autism symptomatology on intervention effects.

Language Ability

As we predicted, our results suggested that intervention effects on language outcomes were dependent on children's entry-level language ability, such that children with higher initial language age equivalency scores at study entry benefited more from language interventions than children with lower initial language age equivalency scores. This finding is consistent with prior evidence and theory that initial developmental achievements in language provide a foundation for further development, creating a “rich get richer” phenomenon, where children with substantial developmental delays benefit less from intervention than their more advanced peers. Further work must be done to develop and test interventions that facilitate improvement for this subgroup of children on the spectrum, especially since recent work suggests that merely intensifying available interventions (i.e., increasing from 5 to 15 hr per week) do not yield added benefit for this group on average ( P. J. Yoder et al., 2018 , 2019 ). There is at least some evidence that suggests that language development in this subgroup of children benefits from more explicit, adult-led interventions than naturalistic, child-led approaches ( Paul et al., 2013 , P. Yoder & Stone, 2006 ).

Outcome Characteristics and Intervention Effects

Language outcome type.

As we anticipated, intervention effects varied depending on the aspect of language that was indexed, such that receptive outcomes were significantly smaller than both expressive and composite language outcomes, when alpha was nominally set at .05. Though expressive language acquisition is often a key focus of intervention for children with autism, these findings add to a large literature suggesting children with autism may have considerable difficulty with receptive language acquisition and may require interventions and Individualized Education Program goals that specifically target receptive skills (e.g., Barbaro & Dissanayake, 2012 ; Charman et al., 2003 ; Ellis Weismer et al., 2010 ; Gillum & Camarata, 2004 ; Luyster et al., 2008 ; Maljaars et al., 2012 ; Pickles et al., 2014 ; Volden et al., 2011 ; Woynaroski et al., 2016 ). Persistent deficits in receptive language may contribute to later academic difficulties such as poor reading comprehension, as well as social difficulties stemming from an inability to follow complex conversational exchanges, negatively impacting long-term outcomes of persons with autism. Thus, it is important that clinicians' focus on acquisition of spoken language for this population does not come at the exclusion of targeting receptive language skills.

It is notable that the expressive language outcomes showed significant evidence for publication and/or small study bias, whereas the receptive language outcomes did not. Given that our search strategy included an extensive gray literature search (see Sandbank et al., 2019 ), it is likely that small study bias or some other factor influenced the expressive language model (see Figure 7 , which shows effect sizes to the right of the funnel have large standard errors, indicating small sample sizes). The presence of bias may have led to inflated summary effect sizes in analyses focused on effects of intervention on expressive outcomes. Therefore, more primary research (or possibly publication of previously completed but presently unpublished primary research) exploring treatment effects on both receptive and expressive language outcomes, particularly rigorous research conducted with large samples, is needed to fully understand the impact of intervention on these domains in young children with autism.

Boundedness and Distality

Though we found evidence in prior work that boundedness and distality moderated effect sizes of intervention across all outcomes of interest to young children with autism, without regard to outcome type ( Sandbank et al., 2019 ), in the present synthesis, we did not find evidence that these outcome characteristics moderated intervention effects on language outcomes specifically. On average, language outcomes that were coded as “potentially context-bound” or generalized were smaller than those coded as context bound, but the confidence intervals for the aforementioned outcome types overlapped. Similarly, language outcomes that were coded as proximal were larger, on average, than those coded as distal, but not significantly different. Our failure to replicate prior findings regarding the effect of boundedness and distality on intervention outcomes may have been a product of reduced power, as fewer outcomes and studies were included in the current analysis compared to that of the larger project (see Sandbank et al., 2019 ). Even so, future investigators should consider these outcome characteristics when planning and interpreting results from intervention studies to ensure that readers do not overestimate an intervention's potential to effect change in outcomes and contexts beyond those that were directly targeted in treatment.

Our claims in regard to the efficacy of interventions on language outcomes should be tempered in light of the quality concerns apparent in the included studies. Similar to the larger meta-analysis from which this subset of study outcomes was drawn, a lack of naive coders and assessors is evident for a majority of outcomes. This quality concern indicates that the summary-level effects we report may be overestimated in comparison to “true” intervention effects, though this issue is unlikely to have affected findings from moderator analyses. Researchers should continue to work toward designing intervention studies that are randomized and increase efforts to ensure that assessors and coders are not aware of intervention assignment.

The multiple strengths of this review article include the use of comprehensive search techniques and advanced meta-analytic tools. First, the wide initial search strategy employed in the larger project likely permitted the identification of more studies than would have been identified in a more targeted search. However, one caveat of this is that, because our search terminology did not specifically include the term “language,” we may have captured studies of interventions that were not specifically language oriented but that simply included measures of language growth and/or outcome as a secondary analysis. However, even if this was the case, presumably, investigators tracked language as a secondary outcome because they had reason to believe that this domain might be distally impacted by the intervention of interest. Thus, we see it as a strength that these effects were also included in this meta-analysis. A second strength of this review article was the use of RVE ( Hedges et al., 2010 ), which allowed us to include multiple language outcomes from each study and then to statistically account for their intercorrelation, increasing the precision of our summary estimates.

Limitations

The primary limitations of this review article are related to power. First, we were unable to test more complex moderation models due to missing data. While the available data were sufficient to justify metaregression models with single moderator terms, we were unable to test models with multiple moderators, because listwise deletion of missing data for any single predictor would have greatly reduced the number of cases in a given model and the corresponding power of that model. Second, most significant moderator results were associated with borderline p values and were no longer significant after correcting for multiple comparisons. Multiple uncorrected comparisons are a common issue in meta-analytic literature and can give rise to Type I errors (i.e., the true effect is zero, but researchers incorrectly reject the null) and skew conclusions about the summary effects of intervention and putative moderators ( Polanin & Pigott, 2015 ). While researchers agree that multiple comparisons can pose a threat, there is less agreement on how to address this threat in meta-analysis ( Borenstein et al., 2011 ). In many cases, researchers choose a lower significance criterion (α) for treating results as significant. Although choosing a more stringent alpha level will reduce the risk of a Type I error, it will also reduce the power to detect an effect and consequently increase the risk of a Type II error (i.e., the true effect is not zero, but researchers fail to reject the null). In balancing the risk for Type I and II errors, researchers must consider the potential consequences of each relative to their own results. For example, if significant results are likely to be interpreted as definitive and trigger an immediate change in practice, then a stringent alpha level should be chosen to avoid a Type I error. Alternatively, if significant results are likely to be interpreted as highlighting potentially important findings and inviting replication, then a less conservative alpha level may be chosen to avoid a Type II error. In regard to the current findings, given that power for tests of moderators in meta-analysis is often already very low ( Hedges & Pigott, 2004 ) and given that our results are unlikely to be interpreted as definitive and produce an immediate change in clinical practice, we believe it would be imprudent to rely on a more stringent alpha criterion when interpreting our results (though we have included corrected p values in Table 3 and explicitly highlighted all instances wherein significant results did not survive corrections for multiple comparisons to allow readers to draw their own conclusions). Rather, we suggest that our results highlight potentially important intervention, participant, and outcome characteristics that may influence intervention effects for children with autism, while also calling for replication.

Finally, an additional limitation of this review article was the forced dichotomization of autism symptomatology. Forced dichotomization of a continuous variable will always result in the loss of information, and in this case, that loss may have obscured a true association. These limitations further prevented us from examining with precision the potential complex interactions between intervention, participant, and outcome characteristics, which might have increased our understanding of “what works” and “for whom” and “for what” ( Sneider, 2018 ).

Future Meta-Analytic Research

Though the results of this meta-analytic investigation are informative, they are limited in scope to language outcomes and should not be assumed to generalize to intervention effects on all outcomes of young children with autism. Moderating associations of intervention, participant, and outcome characteristics on intervention effects likely differ across outcome domains. Thus, future meta-analytic investigations should examine the extent to which these variables moderate intervention effects on other outcomes of interest, particularly those that are core challenges for autistic children, such as social communication and sensory function. It will also be important to examine moderating associations within studies of newer interventions that have recently gathered a large evidence base, such as NDBIs, across outcome types ( Schreibman et al., 2015 ).

Existing interventions can likely affect small but significant improvements in language outcomes on average for children with autism between birth and 8 years of age. Effects are largest for expressive language outcomes and when interventions are delivered by clinicians or clinicians working in combination with caregivers or educators. Children with higher language ability at entry to treatment may stand to make greater gains as a result of intervention than those with more substantial language impairments or lower entry-level language ability. However, some concerns regarding study quality, in particular the high prevalence of assessors and coders who are not naive to group assignment, prevent us from making strong claims in regard to intervention effectiveness. Thus, more rigorous clinical trials are necessary to increase our confidence in the degree to which interventions can facilitate language development for this clinical population in early childhood.

Acknowledgments

This work was funded in part by the Eunice Kennedy Shriver National Institute of Child Health and Human Development (U54HD083211, PI: Neul) and the National Institute on Deafness and Other Communication Disorders (1R21DC016144; Woynaroski). The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding agencies.

Funding Statement

This work was funded in part by the Eunice Kennedy Shriver National Institute of Child Health and Human Development (U54HD083211, PI: Neul) and the National Institute on Deafness and Other Communication Disorders (1R21DC016144; Woynaroski).

1 Though it is standard in journals and professional settings to use person-first language, such as “individuals with autism,” many autistic individuals have endorsed identity-first language, which incorporates autism as a component of identity. Recently, scholars have advocated for the flexible use of identity-first and person-first language and for the avoidance of terms that invoke unnecessary medicalization (e.g., “disorder”) to accommodate the diversity of experiences and opinions of autistic persons and others in the broader autism community ( Robison, 2019 ).

*References marked with an asterisk indicate studies included in the meta-analysis.

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Observation

The littlest linguists: new research on language development.

• Bilingualism
• Developmental Psychology
• Language Development

How do children learn language, and how is language related to other cognitive and social skills? For decades, the specialized field of developmental psycholinguistics has studied how children acquire language—or multiple languages—taking into account biological, neurological, and social factors that influence linguistic developments and, in turn, can play a role in how children learn and socialize. Here’s a look at recent research (2020–2021) on language development published in Psychological Science . 

Preverbal Infants Discover Statistical Word Patterns at Similar Rates as Adults: Evidence From Neural Entrainment

Dawoon Choi, Laura J. Batterink, Alexis K. Black, Ken A. Paller, and Janet F. Werker (2020)

One of the first challenges faced by infants during language acquisition is identifying word boundaries in continuous speech. This neurological research suggests that even preverbal infants can learn statistical patterns in language, indicating that they may have the ability to segment words within continuous speech.

Using electroencephalogram measures to track infants’ ability to segment words, Choi and colleagues found that 6-month-olds’ neural processing increasingly synchronized with the newly learned words embedded in speech over the learning period in one session in the laboratory. Specifically, patterns of electrical activity in their brains increasingly aligned with sensory regularities associated with word boundaries. This synchronization was comparable to that seen among adults and predicted future ability to discriminate words.

These findings indicate that infants and adults may follow similar learning trajectories when tracking probabilities in speech, with both groups showing a logarithmic (rather than linear) increase in the synchronization of neural processing with frequent words. Moreover, speech segmentation appears to use neural mechanisms that emerge early in life and are maintained throughout adulthood.

Parents Fine-Tune Their Speech to Children’s Vocabulary Knowledge

Ashley Leung, Alexandra Tunkel, and Daniel Yurovsky (2021)

Children can acquire language rapidly, possibly because their caregivers use language in ways that support such development. Specifically, caregivers’ language is often fine-tuned to children’s current linguistic knowledge and vocabulary, providing an optimal level of complexity to support language learning. In their new research, Leung and colleagues add to the body of knowledge involving how caregivers foster children’s language acquisition.

The researchers asked individual parents to play a game with their child (age 2–2.5 years) in which they guided their child to select a target animal from a set. Without prompting, the parents provided more informative references for animals they thought their children did not know. For example, if a parent thought their child did not know the word “leopard,” they might use adjectives (“the spotted, yellow leopard”) or comparisons (“the one like a cat”). This indicates that parents adjust their references to account for their children’s language knowledge and vocabulary—not in a simplifying way but in a way that could increase the children’s vocabulary. Parents also appeared to learn about their children’s knowledge throughout the game and to adjust their references accordingly.

Infant and Adult Brains Are Coupled to the Dynamics of Natural Communication

Elise A. Piazza, Liat Hasenfratz, Uri Hasson, and Casey Lew-Williams (2020)

This research tracked real-time brain activation during infant–adult interactions, providing an innovative measure of social interaction at an early age. When communicating with infants, adults appear to be sensitive to subtle cues that can modify their brain responses and behaviors to improve alignment with, and maximize information transfer to, the infants.

Piazza and colleagues used functional near-infrared spectroscopy—a noninvasive measure of blood oxygenation resulting from neural activity that is minimally affected by movements and thus allows participants to freely interact and move—to measure the brain activation of infants (9–15 months old) and adults while they communicated and played with each other. An adult experimenter either engaged directly with an infant by playing with toys, singing nursery rhymes, and reading a story or performed those same tasks while turned away from the child and toward another adult in the room.

Results indicated that when the adult interacted with the child (but not with the other adult), the activations of many prefrontal cortex (PFC) channels and some parietal channels were intercorrelated, indicating neural coupling of the adult’s and child’s brains. Both infant and adult PFC activation preceded moments of mutual gaze and increased before the infant smiled, with the infant’s PFC response preceding the adult’s. Infant PFC activity also preceded an increase in the pitch variability of the adult’s speech, although no changes occurred in the adult’s PFC, indicating that the adult’s speech influenced the infant but probably did not influence neural coupling between the child and the adult.

Theory-of-Mind Development in Young Deaf Children With Early Hearing Provisions

Chi-Lin Yu, Christopher M. Stanzione, Henry M. Wellman, and Amy R. Lederberg (2020)

Language and communication are important for social and cognitive development. Although deaf and hard-of-hearing (DHH) children born to deaf parents can communicate with their caregivers using sign language, most DHH children are born to hearing parents who do not have experience with sign language. These children may have difficulty with early communication and experience developmental delays. For instance, the development of theory of mind—the understanding of others’ mental states—is usually delayed in DHH children born to hearing parents.

Yu and colleagues studied how providing DHH children with hearing devices early in life (before 2 years of age) might enrich their early communication experiences and benefit their language development, supporting the typical development of other capabilities—in particular, theory of mind. The researchers show that 3- to 6-year-old DHH children who began using cochlear implants or hearing aids earlier had more advanced language abilities, leading to better theory-of-mind growth, than children who started using hearing provisions later. These findings highlight the relationships among hearing, language, and theory of mind.

The Bilingual Advantage in Children’s Executive Functioning Is Not Related to Language Status: A Meta-Analytic Review

Cassandra J. Lowe, Isu Cho, Samantha F. Goldsmith, and J. Bruce Morton (2021)

Acommon idea is that bilingual children, who grow up speaking two languages fluently, perform better than monolingual children in diverse executive-functioning domains (e.g., attention, working memory, decision making). This meta-analysis calls that idea into question.

Lowe and colleagues synthesized data from studies that compared the performance of monolingual and bilingual participants between the ages of 3 and 17 years in executive-functioning domains (1,194 effect sizes). They found only a small effect of bilingualism on participants’ executive functioning, which was largely explained by factors such as publication bias. After accounting for these factors, bilingualism had no distinguishable effect. The results of this large meta-analysis thus suggest that bilingual and monolingual children tend to perform at the same level in executive-functioning tasks. Bilingualism does not appear to boost performance in executive functions that serve learning, thinking, reasoning, or problem solving.

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Podcast: In her final column as APS President, Alison Gopnik makes the case for more effectively and creatively caring for vulnerable humans at either end of life.

Communicating Psychological Science: The Lifelong Consequences of Early Language Skills

“When families are informed about the importance of conversational interaction and are provided training, they become active communicators and directly contribute to reducing the word gap (Leung et al., 2020).”

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Archaeologists in Egypt unearth section of large Ramses II statue

A joint Egyptian-U.S. archaeological mission has uncovered the upper part of a huge statue of King Ramses II during excavations south of the Egyptian city of Minya, Egypt's tourism and antiquities ministry said on Monday.

Native Language Loss in Bilinguals Research Paper

The study involves the problems of sociolinguistics since it addresses the problems of loss of native language by bilinguals. The topic is important for the study of language development because, nowadays, the linguistic community is especially aimed at preserving native languages and maintaining linguistic justice. This process is caused by the changes in the attitude of the minorities that are becoming the dominating ideology in Western countries. Bilinguals often experience shame connected with their native language and feel it is inappropriate to use their mother tongue in an official setting, such as in university. It leads to the attrition or loss of the language, which, in turn, reduces the number of languages worldwide. The present research aims to analyze the process of native language loss, in particular, the age when bilinguals cease to use their language and when they start to forget it.

Literature Review

Four sources have been analyzed in terms of preparation for the research described. The first article by Winstead and Wang (2017) addresses the shame that Spanish bilinguals experience while speaking their native language in some environments. It also describes the cases of language loss caused by people’s unwillingness to communicate in their mother tongue. Werker (2012) has analyzed the process of language acquisition in infant bilinguals. The article is relevant for the given research because it analyses the process of learning two languages from the earliest childhood. Levy, McVeigh, Marful, & Anderson (2007) examine the interference produced by the native language that causes the forgetting of the phonology of terms in the native language. Although the study is dedicated to forgetting the native language during the acquisition of a native language, it addresses mostly phonological levels. The given research has to be concentrated more at higher linguistic levels, not only at phonological ones. McFarlane, Cipolletti, & Weissglass (2020) describe bilingualism’s positive effects on cognitive abilities. The researchers provide a profound analysis of the beneficial consequences that it has on decision-making and judgment.

The present research will address the number of questions related to native language attrition in bilinguals. It should analyze in which situations people tend to speak in their mother tongue and which situations are inappropriate for it. The research hypothesizes that bilinguals tend to consider the second language more prestigious and use it in official instances and business environments. If the hypothesis confirms, it will illustrate the linguistic injustice that forces bilinguals to forget their native languages. The study also should analyze the average age at which people tend to cease speaking in their mother tongue. In addition, the research should analyze which exactly language is forgotten. Perhaps, for the languages with large diasporas, the problem of language attrition is not so acute, as in numerous communities, people can speak the native language better than the second one. If it confirms, it will illustrate another side of the language situation and certain injustice that causes language loss and extinction.

Study Design

The study will analyze the natural speech of bilinguals who acquired a second language at an infant age. They can be either speaking one pair of languages or multiple languages. The participants will be randomly selected among children who attend educational facilities in areas with bilingual communities. The hypothesis is that the results will be slightly diverse for different languages. It can be explained by the fact that some diasporas are larger than others, so it is easier for some immigrants to find people who speak their native language. The research has to deal with their communication in a range of conditions. For example, people should remember which situations seem inappropriate for them to speak in their mother tongue.

It is proposed to analyze the bilinguals’ language habits in two steps during the research. During the first step, they can be divided into several groups. They will have to communicate on different topics. In one group, the bilinguals will have to speak about ordinary things, like family, hobbies, and so on. In the second group, the communication has to be more formal and involve business topics. The main confounding factor is that people can realize that they are being analyzed and behave not so naturally as they do in real-life situations. However, it is not expected to be a grave interference factor, as they should be interested in the research and why they forget their native language.

The methods to collect data will be based on qualitative research methods. As the participant will be divided into groups to communicate, the data will be collected with methods such as participant observation (Rahman, 2017). While quantitative research methods seem to be used more broadly than qualitative ones, as the research will be based on the communication process, researchers will be able to interact with participants directly (Rahman, 2017). Although qualitative research techniques analyze participants’ behavior and factors that influence behavior, some statistical analyses are expected to estimate the participants’ use of either language (Rahman, 2017). Finally, certain confounding factors, such as parents’ involvement during research, are needed to be considered to prevent muddling results.

Levy, B. J., McVeigh, N. D., Marful, A., & Anderson, M. C. (2007). Inhibiting your native language: The role of retrieval-induced forgetting during second-language acquisition. Psychological science (0956-7976), 18(1), 29–34. Web.

McFarlane, S. & Cipolletti P., & Weissglass, C. (2020). Thinking in a non-native language: A new nudge? Frontiers in Psychology . Web.

Rahman, M. S. (2017). The advantages and disadvantages of using qualitative and quantitative approaches and methods in language “testing and assessment” research: A literature review. Journal of Education and Learning, 6 (1), 102-112. Web.

Werker, J. (2012). Perceptual foundations of bilingual acquisition in infancy. Annals of the New York Academy of Sciences, 1251 (1), 50–61. Web.

Winstead, L., & Wang, C. (2017). From ELLs to bilingual teachers: Spanish-English speaking Latino teachers’ experiences of language shame & loss. Multicultural education, 24 (3–4), 16–25. Web.

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IvyPanda. (2022, December 15). Native Language Loss in Bilinguals. https://ivypanda.com/essays/native-language-loss-in-bilinguals/

"Native Language Loss in Bilinguals." IvyPanda , 15 Dec. 2022, ivypanda.com/essays/native-language-loss-in-bilinguals/.

IvyPanda . (2022) 'Native Language Loss in Bilinguals'. 15 December.

IvyPanda . 2022. "Native Language Loss in Bilinguals." December 15, 2022. https://ivypanda.com/essays/native-language-loss-in-bilinguals/.

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• Why Bilinguals Are Smarter?
• Phonetic and Phonological Aspects
• Bilinguals’ Cognitive-Linguistic Abilities and Alzheimer’s Disease
• The Second Language Influence on Perception of the Native Language Words
• Language Switching in Bilingual Older Adults
• Challenges of English Learning
• Paweł Zielinski's Report on Bilingualism
• Types of Diasporas: Articles Analysis
• Linguistic Evolution: Language Development
• The Effect of Childhood Bilingualism on Episodic and Semantic
• Variationist Sociolinguistic Study Explanation
• English Diagnostic: Language Proficiency
• Language: The Mean of Learning and Interacting With the World
• “How Language Shapes the Way We Think” by Boroditsky
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• 20 March 2024

Is AI ready to mass-produce lay summaries of research articles?

• Kamal Nahas 0

Kamal Nahas is a freelance science journalist based in Oxford, UK

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Generative AI might be a powerful tool in making research more accessible for scientists and the broader public alike. Credit: Getty

Thinking back to the early days of her PhD programme, Esther Osarfo-Mensah recalls struggling to keep up with the literature. “Sometimes, the wording or the way the information is presented actually makes it quite a task to get through a paper,” says the biophysicist at University College London. Lay summaries could be a time-saving solution. Short synopses of research articles written in plain language could help readers to decide which papers to focus on — but they aren’t common in scientific publishing. Now, the buzz around artificial intelligence (AI) has pushed software engineers to develop platforms that can mass produce these synopses.

Scientists are drawn to AI tools because they excel at crafting text in accessible language, and they might even produce clearer lay summaries than those written by people. A study 1 released last year looked at lay summaries published in one journal and found that those created by people were less readable than were the original abstracts — potentially because some researchers struggle to replace jargon with plain language or to decide which facts to include when condensing the information into a few lines.

AI lay-summary platforms come in a variety of forms (see ‘AI lay-summary tools’). Some allow researchers to import a paper and generate a summary; others are built into web servers, such as the bioRxiv preprint database.

AI lay-summary tools

Several AI resources have been developed to help readers glean information about research articles quickly. They offer different perks. Here are a few examples and how they work:

- SciSummary: This tool parses the sections of a paper to extract the key points and then runs those through the general-purpose large language model GPT-3.5 to transform them into a short summary written in plain language. Max Heckel, the tool’s founder, says it incorporates multimedia into the summary, too: “If it determines that a particular section of the summary is relevant to a figure or table, it will actually show that table or figure in line.”

- Scholarcy: This technology takes a different approach. Its founder, Phil Gooch, based in London, says the tool was trained on 25,000 papers to identify sentences containing verb phrases such as “has been shown to” that often carry key information about the study. It then uses a mixture of custom and open-source large language models to paraphrase those sentences in plain text. “You can actually create ten different types of summaries,” he adds, including one that lays out how the paper is related to previous publications.

- SciSpace: This tool was trained on a repository of more than 280 million data sets, including papers that people had manually annotated, to extract key information from articles. It uses a mixture of proprietary fine-tuned models and GPT-3.5 to craft the summary, says the company’s chief executive, Saikiran Chandha, based in San Francisco, California. “A user can ask questions on top of these summaries to further dig into the paper,” he notes, adding that the company plans to develop audio summaries that people can tune into on the go.

Benefits and drawbacks

Mass-produced lay summaries could yield a trove of benefits. Beyond helping scientists to speed-read the literature, the synopses can be disseminated to people with different levels of expertise, including members of the public. Osarfo-Mensah adds that AI summaries might also aid people who struggle with English. “Some people hide behind jargon because they don’t necessarily feel comfortable trying to explain it,” she says, but AI could help them to rework technical phrases. Max Heckel is the founder of SciSummary, a company in Columbus, Ohio, that offers a tool that allows users to import a paper to be summarized. The tool can also translate summaries into other languages, and is gaining popularity in Indonesia and Turkey, he says, arguing that it could topple language barriers and make science more accessible.

Despite these strides, some scientists feel that improvements are needed before we can rely on AI to describe studies accurately.

Will Ratcliff, an evolutionary biologist at the Georgia Institute of Technology in Atlanta, argues that no tool can produce better text than can professional writers. Although researchers have different writing abilities, he invariably prefers reading scientific material produced by study authors over those generated by AI. “I like to see what the authors wrote. They put craft into it, and I find their abstract to be more informative,” he says.

Is ChatGPT making scientists hyper-productive? The highs and lows of using AI

Nana Mensah, a PhD student in computational biology at the Francis Crick Institute in London, adds that, unlike AI, people tend to craft a narrative when writing lay summaries, helping readers to understand the motivations behind each step of the study. He says, however, that one advantage of AI platforms is that they can write summaries at different reading levels, potentially broadening the audience. In his experience, however, these synopses might still include jargon that can confuse readers without specialist knowledge.

AI tools might even struggle to turn technical language into lay versions at all. Osarfo-Mensah works in biophysics, a field with many intricate parameters and equations. She found that an AI summary of one of her research articles excluded information from a whole section. If researchers were looking for a paper with those details and consulted the AI summary, they might abandon her paper and look for other work.

Andy Shepherd, scientific director at global technology company Envision Pharma Group in Horsham, UK, has in his spare time compared the performances of several AI tools to see how often they introduce blunders. He used eight text generators, including general ones and some that had been optimized to produce lay summaries. He then asked people with different backgrounds, such as health-care professionals and the public, to assess how clear, readable and useful lay summaries were for two papers.

“All of the platforms produced something that was coherent and read like a reasonable study, but a few of them introduced errors, and two of them actively reversed the conclusion of the paper,” he says. It’s easy for AI tools to make this mistake by, for instance, omitting the word ‘not’ in a sentence, he explains. Ratcliff cautions that AI summaries should be viewed as a tool’s “best guess” of what a paper is about, stressing that it can’t check facts.

Broader readership

The risk of AI summaries introducing errors is one concern among many. Another is that one benefit of such summaries — that they can help to share research more widely among the public — could also have drawbacks. The AI summaries posted alongside bioRxiv preprints, research articles that have yet to undergo peer review, are tailored to different levels of reader expertise, including that of the public. Osarfo-Mensah supports the effort to widen the reach of these works. “The public should feel more involved in science and feel like they have a stake in it, because at the end of the day, science isn’t done in a vacuum,” she says.

But others point out that this comes with the risk of making unreviewed and inaccurate research more accessible. Mensah says that academics “will be able to treat the article with the sort of caution that’s required”, but he isn’t sure that members of the public will always understand when a summary refers to unreviewed work. Lay summaries of preprints should come with a “hazard warning” informing the reader upfront that the material has yet to be reviewed, says Shepherd.

Why scientists trust AI too much — and what to do about it

“We agree entirely that preprints must be understood as not peer-reviewed when posted,” says John Inglis, co-founder of bioRxiv, who is based at Cold Spring Harbor Laboratory in New York. He notes that such a disclaimer can be found on the homepage of each preprint, and if a member of the public navigates to a preprint through a web search, they are first directed to the homepage displaying this disclaimer before they can access the summary. But the warning labels are not integrated into the summaries, so there is a risk that these could be shared on social media without the disclaimer. Inglis says bioRxiv is working with its partner ScienceCast, whose technology produces the synopses, on adding a note to each summary to negate this risk.

As is the case for many other nascent generative-AI technologies, humans are still working out the messaging that might be needed to ensure users are given adequate context. But if AI lay-summary tools can successfully mitigate these and other challenges, they might become a staple of scientific publishing.

doi: https://doi.org/10.1038/d41586-024-00865-4

Wen, J. & Yi, L. Scientometrics 128 , 5791–5800 (2023).

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Apple’s MM1 AI Model Shows a Sleeping Giant Is Waking Up

While the tech industry went gaga for generative artificial intelligence , one giant has held back: Apple. The company has yet to introduce so much as an AI-generated emoji, and according to a New York Times report today and earlier reporting from Bloomberg, it is in preliminary talks with Google about adding the search company’s Gemini AI model to iPhones .

Yet a research paper quietly posted online last Friday by Apple engineers suggests that the company is making significant new investments into AI that are already bearing fruit. It details the development of a new generative AI model called MM1 capable of working with text and images. The researchers show it answering questions about photos and displaying the kind of general knowledge skills shown by chatbots like ChatGPT. The model’s name is not explained but could stand for MultiModal 1. MM1 appears to be similar in design and sophistication to a variety of recent AI models from other tech giants, including Meta’s open source Llama 2 and Google’s Gemini . Work by Apple’s rivals and academics shows that models of this type can be used to power capable chatbots or build “agents” that can solve tasks by writing code and taking actions such as using computer interfaces or websites. That suggests MM1 could yet find its way into Apple’s products.

“The fact that they’re doing this, it shows they have the ability to understand how to train and how to build these models,” says Ruslan Salakhutdinov , a professor at Carnegie Mellon who led AI research at Apple several years ago. “It requires a certain amount of expertise.”

MM1 is a multimodal large language model, or MLLM, meaning it is trained on images as well as text. This allows the model to respond to text prompts and also answer complex questions about particular images.

One example in the Apple research paper shows what happened when MM1 was provided with a photo of a sun-dappled restaurant table with a couple of beer bottles and also an image of the menu. When asked how much someone would expect to pay for “all the beer on the table,” the model correctly reads off the correct price and tallies up the cost.

When ChatGPT launched in November 2022, it could only ingest and generate text, but more recently its creator OpenAI and others have worked to expand the underlying large language model technology to work with other kinds of data. When Google launched Gemini (the model that now powers its answer to ChatGPT ) last December, the company touted its multimodal nature as beginning an important new direction in AI. “After the rise of LLMs, MLLMs are emerging as the next frontier in foundation models,” Apple’s paper says.

MM1 is a relatively small model as measured by its number of “parameters,” or the internal variables that get adjusted as a model is trained. Kate Saenko , a professor at Boston University who specializes in computer vision and machine learning, says this could make it easier for Apple’s engineers to experiment with different training methods and refinements before scaling up when they hit on something promising.

Saenko says the MM1 paper provides a surprising amount of detail on how the model was trained for a corporate publication. For instance, the engineers behind MM1 describe tricks for improving the performance of the model including increasing the resolution of images and mixing text and image data. Apple is famed for its secrecy, but it has previously shown unusual openness about AI research as it has sought to lure the talent needed to compete in the crucial technology.

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Saenko says it’s hard to draw too many conclusions about Apple’s plans from the research paper. Multimodal models have proven adaptable to many different use cases. But she suggests that MM1 could perhaps be a step toward building “some type of multimodal assistant that can describe photos, documents, or charts and answer questions about them.”

Apple’s flagship product, the iPhone, already has an AI assistant—Siri. The rise of ChatGPT and its rivals has quickly made the once revolutionary helper look increasingly limited and out-dated. Amazon and Google have said they are integrating LLM technology into their own assistants, Alexa and Google Assistant. Google allows users of Android phones to replace the Assistant with Gemini. Reports from The New York Times and Bloomberg that Apple may add Google’s Gemini to iPhones suggest Apple is considering expanding the strategy it has used for search on mobile devices to generative AI. Rather than develop web search technology in-house, the iPhone maker leans on Google, which reportedly pays more than $18 billion to make its search engine the iPhone default. Apple has also shown it can build its own alternatives to outside services, even when it starts from behind. Google Maps used to be the default on iPhones but in 2012 Apple replaced it with its own maps app .

Apple CEO Tim Cook has promised investors that the company will reveal more of its generative AI plans this year. The company faces pressure to keep up with rival smartphone makers, including Samsung and Google, that have introduced a raft of generative AI tools for their devices.

Apple could end up tapping both Google and its own, in-house AI, perhaps by introducing Gemini as a replacement for conventional Google Search while also building new generative AI tools on top of MM1 and other homegrown models. Last September, several of the researchers behind MM1 published details of MGIE , a tool that uses generative AI to manipulate images based on a text prompt.

Salakhutdinov believes his former employer may focus on developing LLMs that can be installed and run securely on Apple devices. That would fit with the company’s past emphasis on using “on-device” algorithms to safeguard sensitive data and avoid sharing it with other companies. A number of recent AI research papers from Apple concern machine-learning methods designed to preserve user privacy. “I think that's probably what Apple is going to do,” he says.

When it comes to tailoring generative AI to devices, Salakhutdinov says, Apple may yet turn out to have a distinct advantage because of its control over the entire software-hardware stack. The company has included a custom “neural engine” in the chips that power its mobile devices since 2017, with the debut of the iPhone X. “Apple is definitely working in that space, and I think at some point they will be in the front, because they have phones, the distribution.”

In a thread on X, Apple researcher Brandon McKinzie, lead author of the MM1 paper wrote : “This is just the beginning. The team is already hard at work on the next generation of models.”

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Google might let Apple use Gemini, but Apple still has its own LLM coming

• Apple recently published a research paper on a large language model it has been working on.
• The company calls its AI architecture MM1.
• MM1 could be used to build generative AI tools that would run on-device.

When it comes to AI, we’ve seen plenty of products — like ChatGPT and Gemini — from major players in the tech space including Google, Microsoft, and OpenAI. While those companies have been churning out generative AI solutions left and right, Apple has been fairly quiet on this front. But if you thought Apple may be asleep at the wheel, a recently published research paper suggests otherwise.

Apple quietly submitted a research paper last week related to its work on a multimodal large language model (MLLM) called MM1. Apple doesn’t explain what the meaning behind the name is, but it’s possible it could stand for MultiModal 1.

Being multimodal, MM1 is capable of working with both text and images. Overall, its capabilities and design are similar to the likes of Google’s Gemini or Meta’s open-source LLM Llama 2.

An earlier report from Bloomberg said Apple was interested in incorporating Google’s Gemini AI engine into the iPhone. The two companies are reportedly still in talks to let Apple license Gemini to power some of the generative AI features coming to iOS 18.

While Apple attempts to secure that license, it may be planning to use MM1 for other purposes. According to Wired , the Cupertino firm may be angling to use Gemini as a replacement for conventional Google Search. Meanwhile, a former employee who led AI research at Apple, Ruslan   Salakhutdinov, believes the company may focus on building generative AI tools off of MM1 that run on-device, the outlet says.

Another report from Bloomberg last week mentioned that Apple had acquired Canadian AI startup DarwinAI, which specializes in creating smaller and faster AI systems. This is a key factor for on-device processing and could play right into the role Salakhutdinov is suggesting.

It’s still unknown when Apple could start launching these AI products. However, CEO Tim Cook did say during the company’s annual shareholder meeting that AI is already at work behind the scenes in Apple’s products but there would be more news on explicit AI features later this year.

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Computer Science > Computer Vision and Pattern Recognition

Title: mm1: methods, analysis & insights from multimodal llm pre-training.

Abstract: In this work, we discuss building performant Multimodal Large Language Models (MLLMs). In particular, we study the importance of various architecture components and data choices. Through careful and comprehensive ablations of the image encoder, the vision language connector, and various pre-training data choices, we identified several crucial design lessons. For example, we demonstrate that for large-scale multimodal pre-training using a careful mix of image-caption, interleaved image-text, and text-only data is crucial for achieving state-of-the-art (SOTA) few-shot results across multiple benchmarks, compared to other published pre-training results. Further, we show that the image encoder together with image resolution and the image token count has substantial impact, while the vision-language connector design is of comparatively negligible importance. By scaling up the presented recipe, we build MM1, a family of multimodal models up to 30B parameters, including both dense models and mixture-of-experts (MoE) variants, that are SOTA in pre-training metrics and achieve competitive performance after supervised fine-tuning on a range of established multimodal benchmarks. Thanks to large-scale pre-training, MM1 enjoys appealing properties such as enhanced in-context learning, and multi-image reasoning, enabling few-shot chain-of-thought prompting.

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