workshop on research paper writing 2023

Humanities & Social Sciences Writing Workshops

Strategies for Writing About Statistics Caroline Diehl, Psychology, Graduate Writing Consultant This workshop will review how to report a variety of statistical findings in writing, including results from ANOVAs, linear and logistic regression, and t tests, as well as how to use APA formatting for statistics. We will also discuss how to present statistical findings clearly and accurately when writing results and discussion sections. Wednesday, July 19th, 5:15-6:15 PM This workshop will be conducted via Zoom. RSVP on Handshake to receive an invitation: https://ucla.joinhandshake.com/edu/events/1314499

Applying for the NSF Graduate Research Fellowship (General) Various Workshop Presenters This workshop will discuss strategies for applying for the National Science Foundation Graduate Research Fellowship. This highly competitive fellowship provides three years of fellowship funding to successful applicants. The workshop will focus on the intellectual merit and broader impacts criteria for NSF grants, the structure of the research proposal and personal statement, suggestions for writing style, and tips for Fastlane submission. 1) Friday, August 18th, 1:00-2:30 PM This workshop will be conducted remotely via Zoom. RSVP on Handshake to receive an invitation: https://ucla.joinhandshake.com/edu/events/1327812 2) Wednesday, September 6th, 5:15 - 6:45 PM This workshop will be conducted remotely via Zoom. RSVP on Handshake to receive an invitation: https://ucla.joinhandshake.com/edu/events/1327817

Writing Successful Grant and Fellowship Applications (Humanities and Social Sciences) Lika Balenovic, Comparative Literature, Graduate Writing Consultant Jackie Davis, World Arts and Cultures, Graduate Writing Consultant Andrew Malmuth, Sociology, Graduate Writing Consultant Ross Fenimore, Graduate Division Fellowship Services This workshop will first briefly review funding opportunities for graduate students in humanities, social sciences, and related fields. The workshop will then focus on strategies for writing effective applications for grants and fellowships to support graduate study and research, especially for students seeking doctorates and research MAs. We will also cover tips for organizing the application process. Wednesday, September 13th, 4:30-6:00 PM This workshop will be conducted remotely via Zoom. RSVP on Handshake to receive an invitation: https://ucla.joinhandshake.com/edu/events/1327819

Science and Engineering Writing Workshops

Strategies for Writing Scientific Papers Adriana Mendez Leal, Psychology, Graduate Writing Consultant This workshop will focus on strategies for self-editing structure and style in scientific writing. It will also address strategies for improving flow, clarity, and conciseness, as well as general tips for healthy writing. Tuesday, July 18th, 1:00-2:00 PM This workshop will be conducted via Zoom. RSVP on Handshake to receive an invitation: https://ucla.joinhandshake.com/edu/events/1320582

Creating Effective Figures and Visual Aids McKenna Davis, Engineering, Graduate Writing Consultant This workshop focuses on strategies for designing effective figures and visual materials. The workshop will also introduce different software packages that can be used to create high-quality figures and offer further resources for learning these programs. Wednesday, July 19th, 1:00-2:00 PM This workshop will be conducted via Zoom. RSVP on Handshake to receive an invitation: https://ucla.joinhandshake.com/edu/events/1320584

Organizing and Managing Larger Projects McKenna Davis, Engineering, Graduate Writing Consultant1 Adriana Mendez Leal, Psychology, Graduate Writing Consultant This workshop will give an overview of how to organize and manage large scale writing projects, especially in STEM fields. Thursday, July 20th, 1:00-2:00 PM This workshop will be conducted via Zoom. RSVP on Handshake to receive an invitation: https://ucla.joinhandshake.com/edu/events/1320585

Applying for the NSF Graduate Research Fellowship (Engineering and Physical Science Focus) McKenna Davis, Mechanical and Aerospace Engineering, Graduate Writing Consultant Dominique Stumbaugh, Atmospheric and Oceanic Sciences, Graduate Writing Center Consultant Shannon Yu, Graduate Division Fellowship Services This workshop will discuss strategies for applying for the National Science Foundation Graduate Research Fellowship with a physical science focus. This highly competitive fellowship provides three years of fellowship funding to successful applicants. The workshop will focus on the intellectual merit and broader impacts criteria for NSF grants, the structure of the research proposal and personal statement, suggestions for writing style, and tips for Fastlane submission. Tuesday, August 22nd, 4:30-6:00 PM This workshop will be conducted remotely via Zoom. RSVP on Handshake to receive an invitation: https://ucla.joinhandshake.com/edu/events/1327814

Applying for the NSF Graduate Research Fellowship (Life and Bio Science Focus) Paheli Desai-Chowdhry, Biomathematics, Graduate Writing Consultant Aidan Howenstine, Ecology and Evolutionary Biology, Graduate Writing Consultant Leezet Matos, Psychology, Graduate Writing Consultant Shannon Yu, Graduate Division Fellowship Services This workshop will discuss strategies for applying for the National Science Foundation Graduate Research Fellowship with a life science focus. This highly competitive fellowship provides three years of fellowship funding to successful applicants. The workshop will focus on the intellectual merit and broader impacts criteria for NSF grants, the structure of the research proposal and personal statement, suggestions for writing style, and tips for Fastlane submission. Thursday, August 31st, 4:30-6:00 PM This workshop will be conducted remotely via Zoom. RSVP on Handshake to receive an invitation: https://ucla.joinhandshake.com/edu/events/1327816

Writing Successful Grant and Fellowship Applications (STEM Focus) McKenna Davis, Mechanical and Aerospace Engineering, Graduate Writing Consultant Adriana Mendez Leal, Psychology, Graduate Writing Consultant Ross Fenimore, Graduate Division Fellowship Services This workshop will first briefly review funding opportunities for graduate students in STEM fields. The workshop will then focus on strategies for writing effective applications for grants and fellowships to support graduate study and research, especially for students seeking doctorates and research MAs. The workshop will also cover tips for organizing the application process. Tuesday September 12th, 4:30-6:00 PM This workshop will be conducted remotely via Zoom. RSVP on Handshake to receive an invitation: https://ucla.joinhandshake.com/edu/events/1327818

ESL Workshops

Self-Editing Strategies for Non-Native Speakers of English Abraham Encinas, English, Graduate Writing Consultant This workshop will provide concrete strategies and online tools to enable non-native speakers of English to improve their writing and editing process, check grammatical issues, make appropriate vocabulary choices and approach issues like article and preposition usage in their own writing projects. Wednesday, July 26th, 5:15-6:45 PM This workshop will be conducted via Zoom. RSVP on Handshake to receive an invitation: https://ucla.joinhandshake.com/edu/events/1314500

Grants/Fellowships Workshops

Writing the Fulbright U.S. Fellowship Application Jackie Davis, Fulbright Recipient, Graduate Writing Consultant Jessie Stoolman, Fulbright Recipient, Graduate Writing Consultant Shannon Yu, Graduate Division Fellowship Services This workshop will cover strategies for writing the Fulbright U.S. fellowship application. We will discuss the structure and content of key documents using examples. We will address writing tips for fellowship applications as well as specific advice for this Fulbright fellowship. Friday, July 14th, 12:15 PM-1:30 PM This workshop will be conducted via Zoom. RSVP on Handshake to receive an invitation: https://ucla.joinhandshake.com/edu/events/1314501

Applying for the NSF Graduate Research Fellowship Various Workshop Presenters We will offer a series of NSF targeted workshops. The workshop will discuss strategies for applying for the National Science Foundation Graduate Research Fellowship. This highly competitive fellowship provides three years of fellowship funding to successful applicants. The workshop will focus on the intellectual merit and broader impacts criteria for NSF grants, the structure of the research proposal and personal statement, suggestions for writing style, and tips for Fastlane submission. 1) NSF GRF (General) Friday, August 18th, 1:00-2:30 PM This workshop will be conducted remotely via Zoom. RSVP on Handshake to receive an invitation: https://ucla.joinhandshake.com/edu/events/1327812

2) NSF GRF (Engineering and Physical Sciences Focus) Tuesday, August 22nd, 4:30-6:00 PM This workshop will be conducted remotely via Zoom. RSVP on Handshake to receive an invitation: https://ucla.joinhandshake.com/edu/events/1327814

3) NSF GRF (Life and Biological Science Focus) Thursday, August 31st, 4:30-6:00 PM This workshop will be conducted remotely via Zoom. RSVP on Handshake to receive an invitation: https://ucla.joinhandshake.com/edu/events/1327816 4) NSF GRF (General) Wednesday, September 6th, 5:15 - 6:45 PM This workshop will be conducted remotely via Zoom. RSVP on Handshake to receive an invitation: https://ucla.joinhandshake.com/edu/events/1327817

Applying for the NIH Training Fellowship Caroline Diehl, NIH NRSA Recipient, Writing Consultant This workshop will cover the application process and required components of an NIH NRSA application. We will discuss preparation, documents required, and writing strategies for the F31, but the F30 and F32 are very similar. We will also address how to find appropriate materials/resources for a complete application. Thursday, September 28th, 5:15-6:45 PM This workshop will be conducted remotely via Zoom. RSVP on Handshake to receive an invitation: https://ucla.joinhandshake.com/edu/events/1327827

Statistical Software Workshops and Consultation

For walk-in consulting information, Stat books for loan, and the most up-to-date listing of seminars, please see the Statistical Consulting Services web page of the Institute for Digital Research and Education.

Research Workshops at the Library

To see the most complete, current schedule of UCLA Library advanced research workshops, consult the UCLA Library research workshop page .

Research Guides Online research guides link to key resources and reference books for certain courses and disciplines. Subject guides, course guides, and general research guides are available.

Research Appointments Graduate students are encouraged to contact the Library's subject specialists for more specialized and personalized assistance with research, library resources, and subject databases; find a complete list of all subject specialists here .

Copyright Questions Contact the Library's copyright experts for assistance with copyright, publishing, intellectual property, and other related issues at [email protected] .

Announcing the NeurIPS 2023 Workshops

Communications Chairs 2024 2023 Conference

by Hsuan-Tien Lin, Ismini Lourentzou, Piotr Koniusz and Yarin Gal

We are excited to announce the list of NeurIPS 2023 workshops! We received 167 total submissions — a significant increase from last year. From this great batch of submissions, we have accepted 58 workshops that will take place in-person on Dec. 15 & 16. 

We wish we could have accepted many more workshops given the exceptional quality of submissions this year, but technical and logistical constraints meant we could only accept a limited subset. This marks another year that workshop selection had to be selective, and we expect that many of the excellent proposals that we could not accept will be revised, resubmitted, and presented at other ML conferences.

Of course, we want to thank everyone who put in the effort to propose workshops. As Workshop Chairs, all we can do is guide the authors through the submission process. The actual work of organizing the workshops is done by the organizers. Thank you!

New This Year: Fully In-Person Workshops and Proposal Template

In contrast to the previous year, there will not be a separate virtual workshop day. All workshops are anticipated to be conducted in person. Our Call for Post-Conference Workshops emphasizes that NeurIPS will exclusively host in-person workshops this year, accompanied by comprehensive technical support to livestream the workshops to an online audience. We believe that this decision will offer the best experience for both the attendees present in person and those participating online. To assist proposal authors in preparing their proposals and enable reviewers to efficiently evaluate the key aspects of a successful workshop program, we introduced a proposal template this year. The template is experimental and its use is entirely optional for the proposal authors (although we strongly recommended its use). We are pleased to note that over 80% of the proposals adhered to or closely resembled the template. This greatly facilitated the organization and evaluation process, benefiting both proposal authors and reviewers alike.

Refinements: Review Process

We continued to use OpenReview as our submission platform this year, consistent with other NeurIPS submission tracks, due to the success of OpenReview in matching reviewers to proposals. We strived to ensure that every workshop proposal author had an OpenReview profile before the review period started, to better manage conflict-of-interests through OpenReview. Additional details about the selection process are provided below. Except for providing a proposal template , we did not alter requirements for the proposal much this year. We kept the length of the main proposal limited to three pages and the organizer information limited to two pages, along with unlimited references. We let reviewers know that they need not read beyond those pages.

Another point of feedback incorporated was to further refine the reviewing recruitment process. Thus, we increased the reviewer pool, with a focus on including more experienced organizers from past NeurIPS workshops as reviewers. As a result, we sent out over 300 invitations and managed to recruit 127 reviewers. This resulted in at least three reviews per proposal for all 167 proposals. The completion rate for assigned reviews reached 97%, and every proposal received at least three quality reviews. We thank all the reviewers for their timely and professional efforts to provide quality reviews that greatly assisted our decision-making and facilitated an exciting and well-informed workshop program this year.

Selection Process

In making our selections, we asked the reviewers to closely follow our Guidance for Workshop Proposals , which was also shared with the proposal authors. Workshop proposals must be reviewed somewhat differently from academic papers, and we therefore asked the reviewers to consider both scientific merits and broader impacts in their assessments. We recognize that workshop reviews might be somewhat more subjective than academic paper reviews. Following the practice of past years’ review process, we have decided not to release the reviews directly to the proposal authors. However, like last year, we released a short meta-review alongside the decision for each proposal — explaining how the proposal was perceived by the reviewers with the goal of highlighting what could be improved.

Individual evaluations of proposals by reviewers were important for the decision process, but they were not the only considerations in the decision process. For example, we also strived for a good balance between research areas, and between applications and theory. As interest across research areas is not uniform, some areas were more competitive than others. For example, as anticipated, there were many strong proposals surrounding large language models this year. We also received many submissions on important current topics such as privacy, fairness, and causality, as well as the connection between machine learning and other fields. We attempted to balance topics so they would cover both mainstays and emerging topics.

It is also worth noting that we saw many of the pitfalls in proposals also seen in previous years. This included leaning too heavily on past success of existing workshop series, unconfirmed or irrelevant speakers, insufficient time for discussion, scoping too big and too broad, and lip service to diversity.

The next step is for you to contribute! Several workshops have begun soliciting submissions, many using our suggested submission date of September 29, 2023. We typically let each workshop advertise its own call for papers (if they plan to include workshop papers). We will communicate with the workshop organizers some additional deadlines to facilitate the successful planning of 58 exciting workshops. Stay tuned for more technical and contextual information coming soon!

NeurIPS 2023 Accepted Workshops

On to the best part: the preliminary list of accepted workshops for 2023 ( https://neurips.cc/virtual/2023/events/workshop ):

• Backdoors in Deep Learning: The Good, the Bad, and the Ugly
• OPT 2023: Optimization for Machine Learning
• AI for Science: from Theory to Practice
• Generative AI for Education (GAIED): Advances, Opportunities, and Challenges
• Table Representation Learning Workshop
• NeurIPS 2023 Workshop on Machine Learning for Creativity and Design
• Temporal Graph Learning Workshop @ NeurIPS 2023
• NeurIPS 2023 Workshop on Tackling Climate Change with Machine Learning: Blending New and Existing Knowledge Systems
• NeurIPS 2023 Workshop on Generalization in Planning (GenPlan ’23)
• NeurIPS 2023 Workshop on AI for Accelerated Materials Design (AI4Mat-2023)
• Synthetic Data Generation with Generative AI
• NeurIPS 2023 Workshop on Diffusion Models
• The Symbiosis of Deep Learning and Differential Equations — III
• Gaze Meets ML
• Medical Imaging meets NeurIPS
• Information-Theoretic Principles in Cognitive Systems (InfoCog)
• Intrinsically Motivated Open-ended Learning (IMOL) Workshop
• Machine Learning with New Compute Paradigms
• Third Workshop on Efficient Natural Language and Speech Processing (ENLSP-III): The Future of Large Language & Speech Foundation Models
• Workshop on Federated Learning in the Age of Foundation Models in Conjunction with NeurIPS 2023 (FL@FM-NeurIPS’23)
• Heavy Tails in ML: Structure, Stability, Dynamics
• XAI in Action:  Past, Present, and Future Applications
• AI meets Moral Philosophy and Moral Psychology: An Interdisciplinary Dialogue about Computational Ethics
• Agent Learning in Open-Endedness Workshop
• Socially Responsible Language Modelling Research (SoLaR)
• Foundation Models for Decision Making
• Associative Memory & Hopfield Networks in 2023
• Computational Sustainability: Promises and Pitfalls from Theory to Deployment
• MATH-AI: The 3rd Workshop on Mathematical Reasoning and AI
• Optimal Transport and Machine Learning
• Multi-Agent Security: Risks and Opportunities
• The NeurIPS 2023 Workshop on Goal-Conditioned Reinforcement Learning
• NeurIPS 2023 Workshop: Machine Learning and the Physical Sciences
• Workshop on robustness of zero/few-shot learning in foundation models (R0-FoMo)
• NeurIPS 2023 Workshop on Machine Learning for Audio
• Touch Processing: a new Sensing Modality for AI
• 4th Workshop on Self-Supervised Learning: Theory and Practice
• Machine Learning in Structural Biology Workshop
• Regulatable ML: Towards Bridging the Gaps between Machine Learning Research and Regulations
• 6th Robot Learning Workshop: Pretraining, Fine-Tuning, and Generalization with Large Scale Models
• NeurIPS 2023 Workshop on Learning-Based Solutions for Inverse Problems: Opportunities and Challenges
• Workshop on Distribution Shifts: New Frontiers with Foundation Models
• 6th Workshop on Artificial Intelligence for Humanitarian Assistance and Disaster Response
• Mathematics of Modern Machine Learning (M3L)
• I Can’t Believe It’s Not Better (ICBINB): Failure Modes in the Age of Foundation Models
• Generative AI for Biology
• New Frontiers of AI for Drug Discovery and Development
• Symmetry and Geometry in Neural Representations
• Algorithmic Fairness through the Lens of Time
• Machine Learning for Systems
• New Frontiers in Graph Learning (GLFrontiers)
• Adaptive Experimental Design and Active Learning in the Real World
• NeurIPS 2023 Workshop on Instruction Tuning and Instruction Following
• NeurIPS 2023 Workshop on Causal Representation Learning
• Attributing Model Behavior at Scale (ATTRIB)
• Deep Generative Models for Health
• UniReps: Unifying Representations in Neural Models
• Workshop on Advancing Neural Network Training: Computational Efficiency, Scalability, and Resource Optimization

Related Links

• Call for Post-Conference Workshops
• Guidance for Workshop Proposals
• Proposal Template

Related Posts

2023 Conference

Announcing the NeurIPS 2023 Paper Awards 

Announcing neurips 2023 invited talks, reflections on the neurips 2023 ethics review process.

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Nonfiction in Indian Languages

CALL FOR PAPERS

Proposed Title of the book:  Nonfiction in Indian Languages

Concept Note

Call for short stories in English Translation from Indian Languages written by women on Alcohol and Alcoholism

Alcoholism has ruined many families, individuals and relationships. Its impact has not been restricted to just the physical health of the person but has often caused irrevocable harm to the mental health of the individual as well as those associated with the individual, making it as much a health hazard as a social concern. There are several states in India that ban the sale and use of alcohol and are designated dry states. Hooch tragedies are commonly reported.

Call for proposals for Post45 special issue: ‘The potential of cliché’

When we call something a cliché, we’re typically calling it tired, banal, repetitive, or boring. Whether it’s an art object, a turn of speech, or a pattern of behavior, we’re identifying what it lacks: distinctiveness, originality, creativity, thrill. But in pointing to a cliché, we’re also pointing to a response. Noticing cliché creates a fissure. It elicits a reflexive movement, by which we’re forced to reckon with the repetitiousness of language; the ideological and economic structures that shape the creation of art; the social patterns that guide how we relate and self-present. Pointing to cliché, in other words, opens up the possibility for subversion.

CFP: NEPCA Annual Conference Disney Studies Area

Deadline for Submission: June 15, 2024

Contact e-mail: [email protected]

The Northeast Popular Culture Association (NEPCA) Disney Studies Area invites submissions for NEPCA’s annual conference to be held online October 3 – 5, 2024, and in person at Nichols College, MA. Virtual sessions will take place on Thursday evening and Friday morning via Zoom. In-person sessions will take place on Friday evening and Saturday morning with broadcast via Zoom.

Brontë Studies Early Career Research Essay Prize 2024

Brontë Studies  is pleased to invite submissions for the 2024 iteration of the Brontë Studies Early Career Research Essay Prize. The prize aims to encourage new scholarship in the field of Brontë studies, recognise and reward outstanding achievement by new researchers, and support the professional development of the next generation of Brontë scholars. The prize was established in honour of Margaret Smith. She remains one of the most important Brontë scholars of the twentieth and twenty-first centuries.

PSi Invitation--Writing with Thirty Hands: A Workshop On Collaborative Writing in Large Groups

We invite you to participate in a collaborative writing workshop, “Writing with Thirty Hands,” leading up to and meeting at the PSi conference in London.

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About 1 in 5 U.S. teens who’ve heard of ChatGPT have used it for schoolwork

Roughly one-in-five teenagers who have heard of ChatGPT say they have used it to help them do their schoolwork, according to a new Pew Research Center survey of U.S. teens ages 13 to 17. With a majority of teens having heard of ChatGPT, that amounts to 13% of all U.S. teens who have used the generative artificial intelligence (AI) chatbot in their schoolwork.

Teens in higher grade levels are particularly likely to have used the chatbot to help them with schoolwork. About one-quarter of 11th and 12th graders who have heard of ChatGPT say they have done this. This share drops to 17% among 9th and 10th graders and 12% among 7th and 8th graders.

There is no significant difference between teen boys and girls who have used ChatGPT in this way.

The introduction of ChatGPT last year has led to much discussion about its role in schools , especially whether schools should integrate the new technology into the classroom or ban it .

Pew Research Center conducted this analysis to understand American teens’ use and understanding of ChatGPT in the school setting.

The Center conducted an online survey of 1,453 U.S. teens from Sept. 26 to Oct. 23, 2023, via Ipsos. Ipsos recruited the teens via their parents, who were part of its KnowledgePanel . The KnowledgePanel is a probability-based web panel recruited primarily through national, random sampling of residential addresses. The survey was weighted to be representative of U.S. teens ages 13 to 17 who live with their parents by age, gender, race and ethnicity, household income, and other categories.

This research was reviewed and approved by an external institutional review board (IRB), Advarra, an independent committee of experts specializing in helping to protect the rights of research participants.

Here are the  questions used for this analysis , along with responses, and its  methodology .

Teens’ awareness of ChatGPT

Overall, two-thirds of U.S. teens say they have heard of ChatGPT, including 23% who have heard a lot about it. But awareness varies by race and ethnicity, as well as by household income:

• 72% of White teens say they’ve heard at least a little about ChatGPT, compared with 63% of Hispanic teens and 56% of Black teens.
• 75% of teens living in households that make $75,000 or more annually have heard of ChatGPT. Much smaller shares in households with incomes between $30,000 and $74,999 (58%) and less than $30,000 (41%) say the same.

Teens who are more aware of ChatGPT are more likely to use it for schoolwork. Roughly a third of teens who have heard a lot about ChatGPT (36%) have used it for schoolwork, far higher than the 10% among those who have heard a little about it.

When do teens think it’s OK for students to use ChatGPT?

For teens, whether it is – or is not – acceptable for students to use ChatGPT depends on what it is being used for.

There is a fair amount of support for using the chatbot to explore a topic. Roughly seven-in-ten teens who have heard of ChatGPT say it’s acceptable to use when they are researching something new, while 13% say it is not acceptable.

However, there is much less support for using ChatGPT to do the work itself. Just one-in-five teens who have heard of ChatGPT say it’s acceptable to use it to write essays, while 57% say it is not acceptable. And 39% say it’s acceptable to use ChatGPT to solve math problems, while a similar share of teens (36%) say it’s not acceptable.

Some teens are uncertain about whether it’s acceptable to use ChatGPT for these tasks. Between 18% and 24% say they aren’t sure whether these are acceptable use cases for ChatGPT.

Those who have heard a lot about ChatGPT are more likely than those who have only heard a little about it to say it’s acceptable to use the chatbot to research topics, solve math problems and write essays. For instance, 54% of teens who have heard a lot about ChatGPT say it’s acceptable to use it to solve math problems, compared with 32% among those who have heard a little about it.

Note: Here are the  questions used for this analysis , along with responses, and its  methodology .

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Olivia Sidoti is a research assistant focusing on internet and technology research at Pew Research Center

Jeffrey Gottfried is an associate director focusing on internet and technology research at Pew Research Center

Many Americans think generative AI programs should credit the sources they rely on

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RECOMMENDED READS

• 5 Steps to Getting Started with Llama 2
• The Llama Ecosystem: Past, Present, and Future
• Introducing Code Llama, a state-of-the-art large language model for coding
• Meta and Microsoft Introduce the Next Generation of Llama
• Today, we’re introducing Meta Llama 3, the next generation of our state-of-the-art open source large language model.
• Llama 3 models will soon be available on AWS, Databricks, Google Cloud, Hugging Face, Kaggle, IBM WatsonX, Microsoft Azure, NVIDIA NIM, and Snowflake, and with support from hardware platforms offered by AMD, AWS, Dell, Intel, NVIDIA, and Qualcomm.
• We’re dedicated to developing Llama 3 in a responsible way, and we’re offering various resources to help others use it responsibly as well. This includes introducing new trust and safety tools with Llama Guard 2, Code Shield, and CyberSec Eval 2.
• In the coming months, we expect to introduce new capabilities, longer context windows, additional model sizes, and enhanced performance, and we’ll share the Llama 3 research paper.
• Meta AI, built with Llama 3 technology, is now one of the world’s leading AI assistants that can boost your intelligence and lighten your load—helping you learn, get things done, create content, and connect to make the most out of every moment. You can try Meta AI here .

Today, we’re excited to share the first two models of the next generation of Llama, Meta Llama 3, available for broad use. This release features pretrained and instruction-fine-tuned language models with 8B and 70B parameters that can support a broad range of use cases. This next generation of Llama demonstrates state-of-the-art performance on a wide range of industry benchmarks and offers new capabilities, including improved reasoning. We believe these are the best open source models of their class, period. In support of our longstanding open approach, we’re putting Llama 3 in the hands of the community. We want to kickstart the next wave of innovation in AI across the stack—from applications to developer tools to evals to inference optimizations and more. We can’t wait to see what you build and look forward to your feedback.

Our goals for Llama 3

With Llama 3, we set out to build the best open models that are on par with the best proprietary models available today. We wanted to address developer feedback to increase the overall helpfulness of Llama 3 and are doing so while continuing to play a leading role on responsible use and deployment of LLMs. We are embracing the open source ethos of releasing early and often to enable the community to get access to these models while they are still in development. The text-based models we are releasing today are the first in the Llama 3 collection of models. Our goal in the near future is to make Llama 3 multilingual and multimodal, have longer context, and continue to improve overall performance across core LLM capabilities such as reasoning and coding.

State-of-the-art performance

Our new 8B and 70B parameter Llama 3 models are a major leap over Llama 2 and establish a new state-of-the-art for LLM models at those scales. Thanks to improvements in pretraining and post-training, our pretrained and instruction-fine-tuned models are the best models existing today at the 8B and 70B parameter scale. Improvements in our post-training procedures substantially reduced false refusal rates, improved alignment, and increased diversity in model responses. We also saw greatly improved capabilities like reasoning, code generation, and instruction following making Llama 3 more steerable.

*Please see evaluation details for setting and parameters with which these evaluations are calculated.

In the development of Llama 3, we looked at model performance on standard benchmarks and also sought to optimize for performance for real-world scenarios. To this end, we developed a new high-quality human evaluation set. This evaluation set contains 1,800 prompts that cover 12 key use cases: asking for advice, brainstorming, classification, closed question answering, coding, creative writing, extraction, inhabiting a character/persona, open question answering, reasoning, rewriting, and summarization. To prevent accidental overfitting of our models on this evaluation set, even our own modeling teams do not have access to it. The chart below shows aggregated results of our human evaluations across of these categories and prompts against Claude Sonnet, Mistral Medium, and GPT-3.5.

Preference rankings by human annotators based on this evaluation set highlight the strong performance of our 70B instruction-following model compared to competing models of comparable size in real-world scenarios.

Our pretrained model also establishes a new state-of-the-art for LLM models at those scales.

To develop a great language model, we believe it’s important to innovate, scale, and optimize for simplicity. We adopted this design philosophy throughout the Llama 3 project with a focus on four key ingredients: the model architecture, the pretraining data, scaling up pretraining, and instruction fine-tuning.

Model architecture

In line with our design philosophy, we opted for a relatively standard decoder-only transformer architecture in Llama 3. Compared to Llama 2, we made several key improvements. Llama 3 uses a tokenizer with a vocabulary of 128K tokens that encodes language much more efficiently, which leads to substantially improved model performance. To improve the inference efficiency of Llama 3 models, we’ve adopted grouped query attention (GQA) across both the 8B and 70B sizes. We trained the models on sequences of 8,192 tokens, using a mask to ensure self-attention does not cross document boundaries.

Training data

To train the best language model, the curation of a large, high-quality training dataset is paramount. In line with our design principles, we invested heavily in pretraining data. Llama 3 is pretrained on over 15T tokens that were all collected from publicly available sources. Our training dataset is seven times larger than that used for Llama 2, and it includes four times more code. To prepare for upcoming multilingual use cases, over 5% of the Llama 3 pretraining dataset consists of high-quality non-English data that covers over 30 languages. However, we do not expect the same level of performance in these languages as in English.

To ensure Llama 3 is trained on data of the highest quality, we developed a series of data-filtering pipelines. These pipelines include using heuristic filters, NSFW filters, semantic deduplication approaches, and text classifiers to predict data quality. We found that previous generations of Llama are surprisingly good at identifying high-quality data, hence we used Llama 2 to generate the training data for the text-quality classifiers that are powering Llama 3.

We also performed extensive experiments to evaluate the best ways of mixing data from different sources in our final pretraining dataset. These experiments enabled us to select a data mix that ensures that Llama 3 performs well across use cases including trivia questions, STEM, coding, historical knowledge, etc.

Scaling up pretraining

To effectively leverage our pretraining data in Llama 3 models, we put substantial effort into scaling up pretraining. Specifically, we have developed a series of detailed scaling laws for downstream benchmark evaluations. These scaling laws enable us to select an optimal data mix and to make informed decisions on how to best use our training compute. Importantly, scaling laws allow us to predict the performance of our largest models on key tasks (for example, code generation as evaluated on the HumanEval benchmark—see above) before we actually train the models. This helps us ensure strong performance of our final models across a variety of use cases and capabilities.

We made several new observations on scaling behavior during the development of Llama 3. For example, while the Chinchilla-optimal amount of training compute for an 8B parameter model corresponds to ~200B tokens, we found that model performance continues to improve even after the model is trained on two orders of magnitude more data. Both our 8B and 70B parameter models continued to improve log-linearly after we trained them on up to 15T tokens. Larger models can match the performance of these smaller models with less training compute, but smaller models are generally preferred because they are much more efficient during inference.

To train our largest Llama 3 models, we combined three types of parallelization: data parallelization, model parallelization, and pipeline parallelization. Our most efficient implementation achieves a compute utilization of over 400 TFLOPS per GPU when trained on 16K GPUs simultaneously. We performed training runs on two custom-built 24K GPU clusters . To maximize GPU uptime, we developed an advanced new training stack that automates error detection, handling, and maintenance. We also greatly improved our hardware reliability and detection mechanisms for silent data corruption, and we developed new scalable storage systems that reduce overheads of checkpointing and rollback. Those improvements resulted in an overall effective training time of more than 95%. Combined, these improvements increased the efficiency of Llama 3 training by ~three times compared to Llama 2.

Instruction fine-tuning

To fully unlock the potential of our pretrained models in chat use cases, we innovated on our approach to instruction-tuning as well. Our approach to post-training is a combination of supervised fine-tuning (SFT), rejection sampling, proximal policy optimization (PPO), and direct preference optimization (DPO). The quality of the prompts that are used in SFT and the preference rankings that are used in PPO and DPO has an outsized influence on the performance of aligned models. Some of our biggest improvements in model quality came from carefully curating this data and performing multiple rounds of quality assurance on annotations provided by human annotators.

Learning from preference rankings via PPO and DPO also greatly improved the performance of Llama 3 on reasoning and coding tasks. We found that if you ask a model a reasoning question that it struggles to answer, the model will sometimes produce the right reasoning trace: The model knows how to produce the right answer, but it does not know how to select it. Training on preference rankings enables the model to learn how to select it.

Building with Llama 3

Our vision is to enable developers to customize Llama 3 to support relevant use cases and to make it easier to adopt best practices and improve the open ecosystem. With this release, we’re providing new trust and safety tools including updated components with both Llama Guard 2 and Cybersec Eval 2, and the introduction of Code Shield—an inference time guardrail for filtering insecure code produced by LLMs.

We’ve also co-developed Llama 3 with torchtune , the new PyTorch-native library for easily authoring, fine-tuning, and experimenting with LLMs. torchtune provides memory efficient and hackable training recipes written entirely in PyTorch. The library is integrated with popular platforms such as Hugging Face, Weights & Biases, and EleutherAI and even supports Executorch for enabling efficient inference to be run on a wide variety of mobile and edge devices. For everything from prompt engineering to using Llama 3 with LangChain we have a comprehensive getting started guide and takes you from downloading Llama 3 all the way to deployment at scale within your generative AI application.

A system-level approach to responsibility

We have designed Llama 3 models to be maximally helpful while ensuring an industry leading approach to responsibly deploying them. To achieve this, we have adopted a new, system-level approach to the responsible development and deployment of Llama. We envision Llama models as part of a broader system that puts the developer in the driver’s seat. Llama models will serve as a foundational piece of a system that developers design with their unique end goals in mind.

Instruction fine-tuning also plays a major role in ensuring the safety of our models. Our instruction-fine-tuned models have been red-teamed (tested) for safety through internal and external efforts. ​​Our red teaming approach leverages human experts and automation methods to generate adversarial prompts that try to elicit problematic responses. For instance, we apply comprehensive testing to assess risks of misuse related to Chemical, Biological, Cyber Security, and other risk areas. All of these efforts are iterative and used to inform safety fine-tuning of the models being released. You can read more about our efforts in the model card .

Llama Guard models are meant to be a foundation for prompt and response safety and can easily be fine-tuned to create a new taxonomy depending on application needs. As a starting point, the new Llama Guard 2 uses the recently announced MLCommons taxonomy, in an effort to support the emergence of industry standards in this important area. Additionally, CyberSecEval 2 expands on its predecessor by adding measures of an LLM’s propensity to allow for abuse of its code interpreter, offensive cybersecurity capabilities, and susceptibility to prompt injection attacks (learn more in our technical paper ). Finally, we’re introducing Code Shield which adds support for inference-time filtering of insecure code produced by LLMs. This offers mitigation of risks around insecure code suggestions, code interpreter abuse prevention, and secure command execution.

With the speed at which the generative AI space is moving, we believe an open approach is an important way to bring the ecosystem together and mitigate these potential harms. As part of that, we’re updating our Responsible Use Guide (RUG) that provides a comprehensive guide to responsible development with LLMs. As we outlined in the RUG, we recommend that all inputs and outputs be checked and filtered in accordance with content guidelines appropriate to the application. Additionally, many cloud service providers offer content moderation APIs and other tools for responsible deployment, and we encourage developers to also consider using these options.

Deploying Llama 3 at scale

Llama 3 will soon be available on all major platforms including cloud providers, model API providers, and much more. Llama 3 will be everywhere .

Our benchmarks show the tokenizer offers improved token efficiency, yielding up to 15% fewer tokens compared to Llama 2. Also, Group Query Attention (GQA) now has been added to Llama 3 8B as well. As a result, we observed that despite the model having 1B more parameters compared to Llama 2 7B, the improved tokenizer efficiency and GQA contribute to maintaining the inference efficiency on par with Llama 2 7B.

For examples of how to leverage all of these capabilities, check out Llama Recipes which contains all of our open source code that can be leveraged for everything from fine-tuning to deployment to model evaluation.

What’s next for Llama 3?

The Llama 3 8B and 70B models mark the beginning of what we plan to release for Llama 3. And there’s a lot more to come.

Our largest models are over 400B parameters and, while these models are still training, our team is excited about how they’re trending. Over the coming months, we’ll release multiple models with new capabilities including multimodality, the ability to converse in multiple languages, a much longer context window, and stronger overall capabilities. We will also publish a detailed research paper once we are done training Llama 3.

To give you a sneak preview for where these models are today as they continue training, we thought we could share some snapshots of how our largest LLM model is trending. Please note that this data is based on an early checkpoint of Llama 3 that is still training and these capabilities are not supported as part of the models released today.

We’re committed to the continued growth and development of an open AI ecosystem for releasing our models responsibly. We have long believed that openness leads to better, safer products, faster innovation, and a healthier overall market. This is good for Meta, and it is good for society. We’re taking a community-first approach with Llama 3, and starting today, these models are available on the leading cloud, hosting, and hardware platforms with many more to come.

Try Meta Llama 3 today

We’ve integrated our latest models into Meta AI, which we believe is the world’s leading AI assistant. It’s now built with Llama 3 technology and it’s available in more countries across our apps.

You can use Meta AI on Facebook, Instagram, WhatsApp, Messenger, and the web to get things done, learn, create, and connect with the things that matter to you. You can read more about the Meta AI experience here .

Visit the Llama 3 website to download the models and reference the Getting Started Guide for the latest list of all available platforms.

You’ll also soon be able to test multimodal Meta AI on our Ray-Ban Meta smart glasses.

As always, we look forward to seeing all the amazing products and experiences you will build with Meta Llama 3.

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