BerkeleyNLP @BerkeleyNLP
We work on natural language processing, machine learning, linguistics, and deep learning. nlp.cs.berkeley.edu Berkeley, California Joined September 2019-
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New paper from @berkeley_ai on Autonomous Evaluation and Refinement of Digital Agents! We show that VLM/LLM-based evaluators can significantly improve the performance of agents for web browsing and device control, advancing sotas by 29% to 75%. arxiv.org/abs/2404.06474 [🧵]
Do brain representations of language depend on whether the inputs are pixels or sounds? Our @CommsBio paper studies this question from the perspective of language timescales. We find that representations are highly similar between modalities! rdcu.be/dACh5 1/8
We know LLMs hallucinate, but what governs what they dream up? Turns out it’s all about the “unfamiliar” examples they see during finetuning Our new paper shows that manipulating the supervision on these special examples can steer how LLMs hallucinate arxiv.org/abs/2403.05612 🧵
The final layer of an LLM up-projects from hidden dim —> vocab size. The logprobs are thus low rank, and with some clever API queries, you can recover an LLM’s hidden dimension (or even the exact layer’s weights). Our new paper is out, a collaboration between lot of friends!
The final layer of an LLM up-projects from hidden dim —> vocab size. The logprobs are thus low rank, and with some clever API queries, you can recover an LLM’s hidden dimension (or even the exact layer’s weights). Our new paper is out, a collaboration between lot of friends!
What happens when RAG models are provided with documents that have conflicting information? In our new paper, we study how LLMs answer subjective, contentious, and conflicting queries in real-world retrieval-augmented situations.
What Evidence Do Language Models Find Convincing? Finds that LLMs today rely heavily on the relevance of a website to the query, while largely ignoring stylistic features that humans find important such as whether a text contains scientific references arxiv.org/abs/2402.11782
This is a very general flexible & general framework to automatically discover and explain patterns in image dataset. Could be used for ML models, scientific applications, etc. Check it out if you are interested!!
This is a very general flexible & general framework to automatically discover and explain patterns in image dataset. Could be used for ML models, scientific applications, etc. Check it out if you are interested!!
Honored to share our exciting paper on pacing!🎉 #EMNLP2023 Have you suffered overly verbose or vague LLM outputs? 👺 ✨Pacing is vital!✨ We try to improve pacing in long-form story planning.📚 All applause and thanks to my mentor @kevinyang41 first! [1/11]
LLMs can facilitate student cheating, spread misinformation on the web, and even poison future training datasets. Today, we’re releasing Ghostbuster, a state-of-the-art method for detecting LLM-generated text. Paper: arxiv.org/abs/2305.15047 Try it: ghostbuster.app
How should humans supervise AI🤖 if the gold answer is hard to directly verify? My paper on Scalable Oversight has been accepted to EMNLP 2023: "Labeling Programs with Non-Programmers Indirectly via Active Examples: A Case Study with Text-to-SQL" 🧵below
We got fascinating results in this work! * we reverse engineer the training set for Copilot/Codex * we show that data deduplication can sometimes hurt privacy * we reveal the tokenizer of black-box LLMs * we reveal other users' test inputs when adv ex defenses are used
We got fascinating results in this work! * we reverse engineer the training set for Copilot/Codex * we show that data deduplication can sometimes hurt privacy * we reveal the tokenizer of black-box LLMs * we reveal other users' test inputs when adv ex defenses are used
When analyzing ML security and privacy you need to study 𝐬𝐲𝐬𝐭𝐞𝐦𝐬, not just models! Our new paper shows that privacy is way worse when models are deployed in systems that use data cleaners, output filters, etc. Paper: arxiv.org/abs/2309.05610 Blog: spylab.ai/blog/side-chan…
Excited to present SILO, a new nonparametric LM that * excludes copyrighted data from parameters❌ * instead stores it in a datastore and retrieves it at inference time✨ * achieves performance that is close to the model trained on all data🚀 📄arxiv.org/abs/2308.04430
Excited to present SILO, a new nonparametric LM that * excludes copyrighted data from parameters❌ * instead stores it in a datastore and retrieves it at inference time✨ * achieves performance that is close to the model trained on all data🚀 📄arxiv.org/abs/2308.04430
Paper link is here arxiv.org/abs/2308.04430 and this work was led by @ssgrn and @sewon__min!
Feel risky to train your language model on copyrighted data? Check out our new LM called SILO✨, with co-lead @sewon__min Recipe: collect public domain & permissively licensed text data, fit parameters on it, and use the rest of the data in an inference-time-only datastore.
Copyright and legal risks are big open issues in today’s LLMs! In our new paper, we: - curate a pre-training corpus thats legally-permissive - analyze challenges w/ using public domain data - train permissive LMs - propose nonparametric “silos” for data of different legal risks
Copyright and legal risks are big open issues in today’s LLMs! In our new paper, we: - curate a pre-training corpus thats legally-permissive - analyze challenges w/ using public domain data - train permissive LMs - propose nonparametric “silos” for data of different legal risks
How can agents understand the world from diverse language? 🌎 Excited to introduce Dynalang, an agent that learns to understand language by 𝙢𝙖𝙠𝙞𝙣𝙜 𝙥𝙧𝙚𝙙𝙞𝙘𝙩𝙞𝙤𝙣𝙨 𝙖𝙗𝙤𝙪𝙩 𝙩𝙝𝙚 𝙛𝙪𝙩𝙪𝙧𝙚 with a multimodal world model!
Excited to share our new preprint on simulating RLHF preference data more effectively: "RLCD: Reinforcement Learning from Contrast Distillation for Language Model Alignment"! RLCD outperforms strong baselines on three alignment tasks across multiple LLaMA scales. 1/7
[1/9] Large Language Models (LLMs) can mimic humans to explain human decisions. But can they explain THEMSELVEs? How to evaluate explanations along this axis? Check out our work “Do Models Explain Themselves? Counterfactual Simulatability of Natural Language Explanations”!
Excited to announce our ACL Findings 2023 paper (w/ @KevinYa33964384 and Dan Klein): "PREADD: Prefix-Adaptive Decoding for Controlled Text Generation"! PREADD is a prompting-only controlled text generation method, allowing *variable control strength* by contrasting two prompts.
Akari Asai @AkariAsai
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Nils Reimers @Nils_Reimers
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Eric Wallace @Eric_Wallace_
6K Followers 1K Following Researcher at OpenAI working to make language models more trustworthy, secure, and private.Do brain representations of language depend on whether the inputs are pixels or sounds? Our @CommsBio paper studies this question from the perspective of language timescales. We find that representations are highly similar between modalities! rdcu.be/dACh5 1/8
We know LLMs hallucinate, but what governs what they dream up? Turns out it’s all about the “unfamiliar” examples they see during finetuning Our new paper shows that manipulating the supervision on these special examples can steer how LLMs hallucinate arxiv.org/abs/2403.05612 🧵
Google presents: Stealing Part of a Production Language Model - Extracts the projection matrix of OpenAI’s ada and babbage LMs for <$20 - Confirms that their hidden dim is 1024 and 2048, respectively - Also recovers the exact hidden dim size of gpt-3.5-turbo…
The final layer of an LLM up-projects from hidden dim —> vocab size. The logprobs are thus low rank, and with some clever API queries, you can recover an LLM’s hidden dimension (or even the exact layer’s weights). Our new paper is out, a collaboration between lot of friends!
Google presents: Stealing Part of a Production Language Model - Extracts the projection matrix of OpenAI’s ada and babbage LMs for <$20 - Confirms that their hidden dim is 1024 and 2048, respectively - Also recovers the exact hidden dim size of gpt-3.5-turbo…
Future LLMs---whether they be RAG models, chatbots, or agents--will have to sift through misinformation, SEO text, and conflicting opinions when reading text. Alex led an interesting analysis of how current LLMs handle such conflicts. TLDR: LLMs love relevance, not style.
What happens when RAG models are provided with documents that have conflicting information? In our new paper, we study how LLMs answer subjective, contentious, and conflicting queries in real-world retrieval-augmented situations.
We also find that simply prefixing a website's text with "the following text is about [query]" can significantly improve its win-rate. On the other hand, perturbations that target stylistic features of the website, like adding scientific references, have a much weaker effect.
What happens when RAG models are provided with documents that have conflicting information? In our new paper, we study how LLMs answer subjective, contentious, and conflicting queries in real-world retrieval-augmented situations.
What Evidence Do Language Models Find Convincing? Finds that LLMs today rely heavily on the relevance of a website to the query, while largely ignoring stylistic features that humans find important such as whether a text contains scientific references arxiv.org/abs/2402.11782
This is a very general flexible & general framework to automatically discover and explain patterns in image dataset. Could be used for ML models, scientific applications, etc. Check it out if you are interested!!
[1/5] Introducing VisDiff - an #AI tool that describes differences in image sets with natural language. VisDiff can summarize model failures, compare models, find nuanced dataset differences, discover what makes an image memorable, and so much more! …derstanding-visual-datasets.github.io/VisDiff-websit…
Honored to share our exciting paper on pacing!🎉 #EMNLP2023 Have you suffered overly verbose or vague LLM outputs? 👺 ✨Pacing is vital!✨ We try to improve pacing in long-form story planning.📚 All applause and thanks to my mentor @kevinyang41 first! [1/11]
We got fascinating results in this work! * we reverse engineer the training set for Copilot/Codex * we show that data deduplication can sometimes hurt privacy * we reveal the tokenizer of black-box LLMs * we reveal other users' test inputs when adv ex defenses are used
When analyzing ML security and privacy you need to study 𝐬𝐲𝐬𝐭𝐞𝐦𝐬, not just models! Our new paper shows that privacy is way worse when models are deployed in systems that use data cleaners, output filters, etc. Paper: arxiv.org/abs/2309.05610 Blog: spylab.ai/blog/side-chan…
When analyzing ML security and privacy you need to study 𝐬𝐲𝐬𝐭𝐞𝐦𝐬, not just models! Our new paper shows that privacy is way worse when models are deployed in systems that use data cleaners, output filters, etc. Paper: arxiv.org/abs/2309.05610 Blog: spylab.ai/blog/side-chan…
Paper link is here arxiv.org/abs/2308.04430 and this work was led by @ssgrn and @sewon__min!
SILO Language Models: Isolating Legal Risk In a Nonparametric Datastore paper page: huggingface.co/papers/2308.03… The legality of training language models (LMs) on copyrighted or otherwise restricted data is under intense debate. However, as we show, model performance significantly…
Feel risky to train your language model on copyrighted data? Check out our new LM called SILO✨, with co-lead @sewon__min Recipe: collect public domain & permissively licensed text data, fit parameters on it, and use the rest of the data in an inference-time-only datastore.
Excited to present SILO, a new nonparametric LM that * excludes copyrighted data from parameters❌ * instead stores it in a datastore and retrieves it at inference time✨ * achieves performance that is close to the model trained on all data🚀 📄arxiv.org/abs/2308.04430
Feel risky to train your language model on copyrighted data? Check out our new LM called SILO✨, with co-lead @sewon__min Recipe: collect public domain & permissively licensed text data, fit parameters on it, and use the rest of the data in an inference-time-only datastore.
Copyright and legal risks are big open issues in today’s LLMs! In our new paper, we: - curate a pre-training corpus thats legally-permissive - analyze challenges w/ using public domain data - train permissive LMs - propose nonparametric “silos” for data of different legal risks
Feel risky to train your language model on copyrighted data? Check out our new LM called SILO✨, with co-lead @sewon__min Recipe: collect public domain & permissively licensed text data, fit parameters on it, and use the rest of the data in an inference-time-only datastore.
Excited to share our new preprint on simulating RLHF preference data more effectively: "RLCD: Reinforcement Learning from Contrast Distillation for Language Model Alignment"! RLCD outperforms strong baselines on three alignment tasks across multiple LLaMA scales. 1/7
I’m at #ICML2023 this week presenting work on poisoning LLMs and analyzing model pre-training data! Would love to chat about all things LLMs, especially on aspects like robustness/memorization/security/privacy. Feel free to DM or email.
We analyze 14 lang pairs to find when translation requires context. Our thematic analysis identifies 5 discourse phenomena, and we build the MuDA benchmark to automatically tag them. An exciting new resource to evaluate document-level MT on any data, check it out @aclmeeting!
Document-level context is essential to close the gap between MT and humans. But which words require context to be translated? And do models translate them well? Check our MuDA benchmark, for 14 LPs! To appear at #ACL2023! (co-lead @kayo_yin) arxiv.org/abs/2109.07446 1/15
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