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20242026
most citedIs Model Collapse Inevitable? Breaking the Curse of Recursion by Accumulating Real and Synthetic Data

12 citations · 19 across the 6 of their papers we have counts for

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5 papers · 1 filter

cs.CL2024

Best-of-N Jailbreaking

John Hughes, Sara Price, Aengus Lynch +7

We introduce Best-of-N (BoN) Jailbreaking, a simple black-box algorithm that jailbreaks frontier AI systems across modalities. BoN Jailbreaking works by repeatedly sampling variati…

cs.LG2024

Jailbreak Defense in a Narrow Domain: Limitations of Existing Methods and a New Transcript-Classifier Approach

Tony T. Wang, John Hughes, Henry Sleight +7

Defending large language models against jailbreaks so that they never engage in a broadly-defined set of forbidden behaviors is an open problem. In this paper, we investigate the d…

cs.CL2024★ 7 cited

Looking Inward: Language Models Can Learn About Themselves by Introspection

Felix J Binder, James Chua, Tomek Korbak +6

Humans acquire knowledge by observing the external world, but also by introspection. Introspection gives a person privileged access to their current state of mind (e.g., thoughts a…

cs.CL2024

Failures to Find Transferable Image Jailbreaks Between Vision-Language Models

Rylan Schaeffer, Dan Valentine, Luke Bailey +12

The integration of new modalities into frontier AI systems offers exciting capabilities, but also increases the possibility such systems can be adversarially manipulated in undesir…

cs.LG2024★ 12 cited

Is Model Collapse Inevitable? Breaking the Curse of Recursion by Accumulating Real and Synthetic Data

Matthias Gerstgrasser, Rylan Schaeffer, Apratim Dey +11

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 out…