Yejin Choi @YejinChoinka
professor at UW, director at AI2, adventurer at heart homes.cs.washington.edu/~yejin/ Seattle, WA Joined August 2017-
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We created reviewing guidelines for @COLM_conf. Not intended to automate the committee work, or dictate constraints. But, to inspire a thoughtful reviewing process, for an exciting and impactful program of the highest possible quality. We have a wonderful program committee ❤️
Yes, both the 8B and 70B are trained way more than is Chinchilla optimal - but we can eat the training cost to save you inference cost! One of the most interesting things to me was how quickly the 8B was improving even at 15T tokens.
Yes, both the 8B and 70B are trained way more than is Chinchilla optimal - but we can eat the training cost to save you inference cost! One of the most interesting things to me was how quickly the 8B was improving even at 15T tokens.
We took this on Day2 of #TED2024. Some #AI ROCK⭐️'s...@drfeifei Daniela Rus @MIT_CSAIL @hlntnr @YejinChoinka @ruchowdh Niceaunties. And speaking today @CatieCuan + @AnimaAnandkumar ...oh...and then there's me🤣
Just tried the new GPT4+v on our New Yorker caption contest task (arxiv.org/abs/2209.06293). It does OK! (70%, good for second on leaderboard). But, w/ performance ~25% below human, it still doesn't quite "get the joke". Maybe your model does? :-) capcon.dev
Introducing our best OLMo yet. OLMo 1.7-7B outperforms LLaMa2-7B, approaching LLaMa2-13B at MMLU and GSM8k. High-quality data and staged training are key. I am so proud of our team making such significant improvement in a short period after our first release.
Introducing our best OLMo yet. OLMo 1.7-7B outperforms LLaMa2-7B, approaching LLaMa2-13B at MMLU and GSM8k. High-quality data and staged training are key. I am so proud of our team making such significant improvement in a short period after our first release. https://t.co/9NNwCxAwj6
We released this new agenda on LLM-safety yesterday. This is VERY comprehensive covering 18 different challenges. My co-authors have posted tweets for each of these challenges. I am going to collect them all here! P.S. this is also now on arxiv: arxiv.org/abs/2404.09932
We released this new agenda on LLM-safety yesterday. This is VERY comprehensive covering 18 different challenges. My co-authors have posted tweets for each of these challenges. I am going to collect them all here! P.S. this is also now on arxiv: arxiv.org/abs/2404.09932
I’m super excited to release our 100+ page collaborative agenda - led by @usmananwar391 - on “Foundational Challenges In Assuring Alignment and Safety of LLMs” alongside 35+ co-authors from NLP, ML, and AI Safety communities! Some highlights below...
I will be talking about what differential privacy is, what it is not and what some common misconceptions are in privacy for generative AI in a couple hours @genlawcenter in DC! Join us on the live stream: tinyurl.com/genlaw-stream Slides: tinyurl.com/genlaw-dp-2024
I will be talking about what differential privacy is, what it is not and what some common misconceptions are in privacy for generative AI in a couple hours @genlawcenter in DC! Join us on the live stream: tinyurl.com/genlaw-stream Slides: tinyurl.com/genlaw-dp-2024 https://t.co/s17N1h4P3m
🥰Excited to share that I will be joining AI2 @allen_ai @ai2_mosaic this September as a predoctoral young investigator!! So excited to continue working with amazing @YejinChoinka @nouhadziri @liweijianglw @kavel_r and can't wait to collaborate with others!
Can we uncover memorization of pre-training data in LLMs, using other LLMs? Our iterative prompt optimization method finds prompts that propel an LM to output training data using other LMs. We show higher avg. data reconstruction & extract 1.4X more PII! arxiv.org/abs/2403.04801
The infini-gram paper is updated with the incredible feedback from the online community 🧡 We added references to papers of @JeffDean @yeewhye @EhsanShareghi @EdwardRaffML et al. arxiv.org/abs/2401.17377 Also happy to share that the infini-gram API has served 30 million queries!
Welcome to the new era of AI: "Deep" was once the buzzword at AI conferences, but it's no longer the case in COLM.
Welcome to the new era of AI: "Deep" was once the buzzword at AI conferences, but it's no longer the case in COLM. https://t.co/xo1soRwMjI
Version II of the tutorial on neural theorem proving: github.com/cmu-l3/ntptuto… Some new additions - Train a model that gets 29.5% on miniF2F - Data extraction in Lean, based on lean-training-data - LLMLean tool (github.com/cmu-l3/llmlean)
Version II of the tutorial on neural theorem proving: github.com/cmu-l3/ntptuto… Some new additions - Train a model that gets 29.5% on miniF2F - Data extraction in Lean, based on lean-training-data - LLMLean tool (github.com/cmu-l3/llmlean) https://t.co/Nm6DtxEHZ4
Updates of ⚔️𝕎𝕚𝕝𝕕𝕍𝕚𝕤𝕚𝕠𝕟-𝔸𝕣𝕖𝕟𝕒: We added more models such as @AnthropicAI's Claude3 and @RekaAILabs! Also, many new features for improving user experience and collecting better evaluation data. E.g., we support selecting models for sampling and inputting reasons…
A few thoughts on using a DPO model as a reward model with reference model. It's a losing battle because if the texts are different lengths there's a fundamental signal-to noise problem. Averaging, norm, sum, whatever, will make it hard to extract the signal from a list of…
Do you need free access to GPT-4? It's here 😎huggingface.co/spaces/yuntian… 🌟
Do you need free access to GPT-4? It's here 😎huggingface.co/spaces/yuntian… 🌟
@yoavartzi @COLM_conf Presenting: Breakin' It Down at COLM
I hear text-to-music is having its moment. Can someone generate a theme song for @COLM_conf ? I am not musical enough to even prompt it
LLM often could not correct its own mistakes. However, using a fine-grained feedback model, we could teach LLM how to correct its incorrect generation. Introducing LLMRefine: the power of simulated annealing on top of fine-grained feedback! Check out: arxiv.org/abs/2311.09336
LLM often could not correct its own mistakes. However, using a fine-grained feedback model, we could teach LLM how to correct its incorrect generation. Introducing LLMRefine: the power of simulated annealing on top of fine-grained feedback! Check out: arxiv.org/abs/2311.09336
DBRX-Base from @databricks also achieves the top position in the URIAL Bench, which tests Base LLMs on the MT-bench with URIAL prompts (3-shot instruction-following examples). Check out the full results here on @huggingface 🤗: huggingface.co/spaces/allenai… Related Xs: 1️⃣ [URIAL…
(((ل()(ل() 'yoav))).. @yoavgo
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Jacob Andreas @jacobandreas
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Yoav Artzi @yoavartzi
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William Wang @WilliamWangNLP
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Felix Hill @FelixHill84
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Sara Gemelli @saragemelli_
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Danial Namazifard @IamDanialNamazi
70 Followers 463 Following MSc Student in AI, NLP Researcher @ UT #NLProc #MachineLearning
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Jisang Park @jacejisangpark
0 Followers 27 Following Robotics Research Associate @ Korea Advanced Institute of Science and Technology. Interested in embodied NLP and HRI for personal robots.
Nikita @nikitavoloboev
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Dr. Catie Cuan @CatieCuan
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Agam A. Shah @shahagam4
354 Followers 490 Following Quantitative Finance and NLProc, Georgia Tech | DA-IICT'19. Tweets my own & CC BY-SA 4.0.
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71 Followers 257 Following I'm from Hong Kong, I travel a lot, I've been to every country in the world, I went to Iceland to see the Northern lights, I went to Denmark to see fairy tales.
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Yu-Min Tseng @ym_tseng
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69 Followers 278 Following PhD UT Austin || ex Applied Scientist Amazon Alexa AI || Research interns at Meta, MSR ; TTIC || Researcher in ML Theory, NLP, Optimization,
Helen Toner @hlntnr
21K Followers 1K Following Interests: China+ML, natsec+tech, brains+words+absurdity | Current: @CSETGeorgetown (opinions my own) | Former: @open_phil
Alberto Tono @albertotono3
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(((ل()(ل() 'yoav))).. @yoavgo
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Percy Liang @percyliang
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16K Followers 894 Following Professor @nyuling and @NYUDataScience, research scientist @GoogleAI
Luca Soldaini 🎀 @soldni
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Felix Hill @FelixHill84
9K Followers 776 Following Research Scientist, Deepmind I try to think hard about everything I tweet, esp on 90s football and 80s music None of my opinions are really someone else's
Ana Marasović @anmarasovic
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Allen Institute for A.. @allen_ai
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Conference on Languag.. @COLM_conf
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Stella Biderman @BlancheMinerva
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Nathan Lambert @natolambert
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Jonathan Frankle @jefrankle
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We created reviewing guidelines for @COLM_conf. Not intended to automate the committee work, or dictate constraints. But, to inspire a thoughtful reviewing process, for an exciting and impactful program of the highest possible quality. We have a wonderful program committee ❤️
Llama3 8B is trained on almost 100 times the Chinchilla optimal number of tokens
Yes, both the 8B and 70B are trained way more than is Chinchilla optimal - but we can eat the training cost to save you inference cost! One of the most interesting things to me was how quickly the 8B was improving even at 15T tokens.
Llama3 8B is trained on almost 100 times the Chinchilla optimal number of tokens
We took this on Day2 of #TED2024. Some #AI ROCK⭐️'s...@drfeifei Daniela Rus @MIT_CSAIL @hlntnr @YejinChoinka @ruchowdh Niceaunties. And speaking today @CatieCuan + @AnimaAnandkumar ...oh...and then there's me🤣
Excited to share a preview of Llama3, including the release of an 8B and 70B (82 MMLU, should be the best open weights model!), and preliminary results for a 405B model (still training, but already competitive with GPT4). Lots more still to come... ai.meta.com/blog/meta-llam…
Just tried the new GPT4+v on our New Yorker caption contest task (arxiv.org/abs/2209.06293). It does OK! (70%, good for second on leaderboard). But, w/ performance ~25% below human, it still doesn't quite "get the joke". Maybe your model does? :-) capcon.dev
Introducing our best OLMo yet. OLMo 1.7-7B outperforms LLaMa2-7B, approaching LLaMa2-13B at MMLU and GSM8k. High-quality data and staged training are key. I am so proud of our team making such significant improvement in a short period after our first release.
Announcing our latest addition to the OLMo family, OLMo 1.7!🎉Our team's efforts to improve data quality, training procedures and model architecture have led to a leap in performance. See how OLMo 1.7 stacks up against its peers and peek into the technical details on the blog:…
This is one of the most interesting papers I have seen in a while: arxiv.org/abs/2310.01929… These folks manage to convincingly conceptualize how text-to-image models capture cultural differences.
@jlibovicky @mor_ventura95 @bd_eyal @annalkorhonen @roireichart New relevant paper arxiv.org/abs/2404.10199 by @YejinChoinka @huihan_li (not citing the other one, but I still find the two relevant to each other)
We released this new agenda on LLM-safety yesterday. This is VERY comprehensive covering 18 different challenges. My co-authors have posted tweets for each of these challenges. I am going to collect them all here! P.S. this is also now on arxiv: arxiv.org/abs/2404.09932
I’m super excited to release our 100+ page collaborative agenda - led by @usmananwar391 - on “Foundational Challenges In Assuring Alignment and Safety of LLMs” alongside 35+ co-authors from NLP, ML, and AI Safety communities! Some highlights below...
The 2024 AI Index tacitly shows Natural Language Processing rising to be the central technology of AI 2004: NLP way off in the AI margins 2014: A little excitement over chatbots 2024: AI Index leads with impressive progress of LLMs aiindex.stanford.edu/report/ #NLProc #BiasedTakes
Usman deserves so much credit for leading and organizing this effort! It's been a long haul, but I'm really happy with the result!
Super excited about the release of this 🔥agenda paper on “Foundational Challenges in Assuring Alignment and Safety of LLMs!” that has been described as ‘particularly comprehensive' and 'epic piece of work' in private reviews. 😅
Excited about this for many reasons, but the biggest are 1. T5 is very very widely used IRL and better models are a good thing. 2. Checkpoints saved every 10,000 steps enabling research on learning dynamics and interp for s2s models like what Pythia has done for decoder models.
🚀 Introducing Pile-T5! 🔗 We (EleutherAI) are thrilled to open-source our latest T5 model trained on 2T tokens from the Pile using the Llama tokenizer. ✨ Featuring intermediate checkpoints and a significant boost in benchmark performance. Work done by @lintangsutawika, me…
I’m super excited to release our 100+ page collaborative agenda - led by @usmananwar391 - on “Foundational Challenges In Assuring Alignment and Safety of LLMs” alongside 35+ co-authors from NLP, ML, and AI Safety communities! Some highlights below...
I will be talking about what differential privacy is, what it is not and what some common misconceptions are in privacy for generative AI in a couple hours @genlawcenter in DC! Join us on the live stream: tinyurl.com/genlaw-stream Slides: tinyurl.com/genlaw-dp-2024
So excited to announce an event @genlawcenter has been working on! We're discuss the misconceptions b/w the technical capabilities of evaluating generative AI, and what policymakers and civil society want... April 15th @GtownTechLaw, and live on zoom: dc-workshop.genlaw.org
Thanks for the shout out! We'll be updating the course website with materials as the term progresses, and hope others find it useful. We're also thrilled to have guest lectures by @tydsh, @denny_zhou, @KaiyuYang4!
CS159: LLMs for reasoning lecture slides from Caltech are really good. Link: sites.google.com/view/cs-159-20… Thank you for making them public @yisongyue and @acbuller
Defended my thesis yesterday :) Its been a fantastic ride at @columbianlp and I am grateful to my advisor @SmaraMuresanNLP for believing in my work. Special thanks to @VioletNPeng who introduced me to Creative NLG which made a lot of the work in my thesis possible
I’m an Outstanding Senior Researcher @gtcomputing. Anyone who knows anything knows that it’s the members of my lab that really won this award. Congratulations, fam!
End of year outing for the GLAMOR ✨ Lab!
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