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Found 7 papers, 4 papers with code
Best-of-N Jailbreaking
We find that BoN Jailbreaking achieves high attack success rates (ASRs) on closed-source language models, such as 89% on GPT-4o and 78% on Claude 3. 5 Sonnet when sampling 10, 000 augmented prompts.
Jailbreak Defense in a Narrow Domain: Limitations of Existing Methods and a New Transcript-Classifier Approach
Defending large language models against jailbreaks so that they never engage in a broadly-defined set of forbidden behaviors is an open problem.
Looking Inward: Language Models Can Learn About Themselves by Introspection
We study introspection by finetuning LLMs to predict properties of their own behavior in hypothetical scenarios.
Failures to Find Transferable Image Jailbreaks Between Vision-Language Models
These results stand in stark contrast to existing evidence of universal and transferable text jailbreaks against language models and transferable adversarial attacks against image classifiers, suggesting that VLMs may be more robust to gradient-based transfer attacks.
Is Model Collapse Inevitable? Breaking the Curse of Recursion by Accumulating Real and Synthetic Data
The proliferation of generative models, combined with pretraining on web-scale data, raises a timely question: what happens when these models are trained on their own generated outputs?
Debating with More Persuasive LLMs Leads to More Truthful Answers
In anticipation of this, we ask: can weaker models assess the correctness of stronger models?
Hierarchical Quantized Autoencoders
Despite progress in training neural networks for lossy image compression, current approaches fail to maintain both perceptual quality and abstract features at very low bitrates.
