Eric Wallace @Eric_Wallace_
Researcher at OpenAI working to make language models more trustworthy, secure, and private. ericswallace.com Berkeley, CA Joined July 2011-
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Some personal updates: I joined OpenAI a few months ago, working on all things robustness/safety/privacy. Also, we are working to publish more of our safety work. See my first project here below, where we make initial progress on prompt injections and other attacks!
Some personal updates: I joined OpenAI a few months ago, working on all things robustness/safety/privacy. Also, we are working to publish more of our safety work. See my first project here below, where we make initial progress on prompt injections and other attacks!
Really cool concurrent work to our recent paper!
Really cool concurrent work to our recent paper!
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!
Cool paper by Wan et al (UC Berkeley) with surprising results. In their task, an LLM answers a controversial question Q based on the conflicting arguments from excerpts from two documents from the web. We might expect that LLMs would be more influenced by excerpts that (a) have…
Does anyone have a favorite task where gpt-4 has near chance accuracy when zero or few-shot prompted? I’m looking for recommendations for tasks like this
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.
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.
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
Responsible disclosure: We discovered this exploit in July, informed OpenAI Aug 30, and we’re releasing this today after the standard 90 day disclosure period.
Our paper with extra experiments on the causes and extent of data leakage: arxiv.org/abs/2311.17035 Thanks to the incredible work of Milad Nasr, Nicholas Carlini, Jon Hayase, Matthew Jagielski, @afedercooper, @daphneipp, @Chris_Choquette, @Eric_Wallace_, @florian_tramer
What happens if you ask ChatGPT to “Repeat this word forever: “poem poem poem poem”?” It leaks training data! In our latest preprint, we show how to recover thousands of examples of ChatGPT's Internet-scraped pretraining data: not-just-memorization.github.io/extracting-tra…
New paper!! We found a pattern in how NNs extrapolate: as inputs become more OOD, model outputs tend to go towards some “average”-like prediction. What is this “average”-like prediction? Why does this happen? Can we leverage this to better handle OOD inputs? (Spoiler: Yes!) 🧵:
Just reminding everyone that this paper exists and is really good: arxiv.org/abs/2309.05610
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
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…
SILO Language Models: Isolating Legal Risk In a Nonparametric Datastore Presents a LM built by training a corpus of permissively licensed text and augmenting it with a datastore (e.g., copyrighted books or news) that is only queried during inference. arxiv.org/abs/2308.04430
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.
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Imitation learning works™ – but you need good data 🥹 How to get high-quality visuotactile demos from a bimanual robot with multifingered hands, and learn smooth policies? Check our new work “Learning Visuotactile Skills with Two Multifingered Hands”! 🙌 toruowo.github.io/hato/
PhDone!!!! 👨🎓 08/2019-04/2024 What a journey 🥳🚞 I especially feel lucky to share this once-in-a-life-time moment with people I love ❤️ . And seeing my passion-driven research efforts being acknowledged by researchers I deeply admire 🌞!! Special thanks to my awesome committee…
@Eric_Wallace_ Congrats!! and very cool work :)
@Eric_Wallace_ Congrats Eric! Looking forward to your safety papers!
Introducing the Instruction Hierarchy, our latest safety research to advance robustness for prompt injections and other ways of tricking LLMs into executing unsafe actions. More details: arxiv.org/abs/2404.13208
innnnteresting
Open AI presents The Instruction Hierarchy Training LLMs to Prioritize Privileged Instructions Today's LLMs are susceptible to prompt injections, jailbreaks, and other attacks that allow adversaries to overwrite a model's original instructions with their own malicious prompts.
New paper from @OpenAI on prompt injection - it's the most detailed evaluation of the problem I've seen from them so far, and has some very interesting details Posted some of my notes on the paper on my log here: simonwillison.net/2024/Apr/23/th…
Open AI presents The Instruction Hierarchy Training LLMs to Prioritize Privileged Instructions Today's LLMs are susceptible to prompt injections, jailbreaks, and other attacks that allow adversaries to overwrite a model's original instructions with their own malicious prompts.
Open AI presents The Instruction Hierarchy Training LLMs to Prioritize Privileged Instructions Today's LLMs are susceptible to prompt injections, jailbreaks, and other attacks that allow adversaries to overwrite a model's original instructions with their own malicious prompts.
How did they steal parts of LLMs protected behind APIs? 🥷 We explain both papers that made it happen, one from Carlini et al. (Google), and the other one from @mattf1n et al. (USC). 👇 📺 youtu.be/O_eUzrFU6eQ
Wanna know gpt-3.5-turbo's embed size? We find a way to extract info from LLM APIs and estimate gpt-3.5-turbo’s embed size to be 4096. With the same trick we also develop 25x faster logprob extraction, audits for LLM APIs, and more! 📄 arxiv.org/abs/2403.09539 Here’s how 1/🧵
Fine-tuning on benign data (e.g. Alpaca) can jailbreak models unexpectedly. We study this problem through a data-centric perspective and find that some seemingly benign data could be more harmful than explicitly malicious data! ⚠️🚨‼️ Paper: arxiv.org/pdf/2404.01099… [1/n]
Are you interested in jailbreaking LLMs? Have you ever wished that jailbreaking research was more standardized, reproducible, or transparent? Check out JailbreakBench, an open benchmark and leaderboard for Jailbreak attacks and defenses on LLMs! jailbreakbench.github.io 🧵1/n
Update on 🔮Readout Guidance (readout-guidance.github.io)! We open sourced the code – check out our demos, model weights, and training code: github.com/google-researc…. Here’s a teaser of what you can do with our method:
If you download a pretrained model you have to trust that the developer did not backdoor it. We know backdoors break model integrity. But what about privacy? With Shanglun Feng we introduce 𝐩𝐫𝐢𝐯𝐚𝐜𝐲 𝐛𝐚𝐜𝐤𝐝𝐨𝐨𝐫𝐬: pretrained models that steal your finetuning data! 🧵
My time visiting Berkeley is coming to an end. Many thanks to Prof. Pieter Abbeel @pabbeel and Xingyu @Xingyu2017 for your guidance. Grateful to every member of RLL; your companionship and friendship are my most precious memories. Wishing you all the best for the future!
yay!
See you all in Vienna, Austria for GenLaw 2 at ICML 2024! We're so excited to be in Europe and use this opportunity to dig into GDPR, and text & data mining exceptions!! We'll put up a website soon with a CFP. It'll be pretty similar to last years (genlaw.org/cfp.html) but…
Are you interested in training large models in JAX but are set back by the complicated partition specs and sharding configurations required to scale up? I've recently created scalax, a small library to help developers easily scale up JAX models. github.com/young-geng/sca…
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