Summary of Evaluating Defences Against Unsafe Feedback in Rlhf, by Domenic Rosati et al.
Evaluating Defences against Unsafe Feedback in RLHF
by Domenic Rosati, Giles Edkins, Harsh Raj, David Atanasov, Subhabrata Majumdar, Janarthanan Rajendran, Frank Rudzicz, Hassan Sajjad
First submitted to arxiv on: 19 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed study investigates the vulnerability of Large Language Models (LLMs) to harmful feedback during reinforcement learning. Specifically, it examines how safety-aligned LLMs can be tricked into generating unsafe text by optimizing for rewards that violate safety constraints. The authors analyze various learning settings where feedback is harmful and find that current safety guards are insufficient to prevent this vulnerability. To mitigate this issue, the study evaluates several “implicit” and “explicit” fine-tuning defenses against harmful feedback, concluding that no single method is effective in preventing LLMs from learning unsafe behavior. The findings highlight the need for further research on defense strategies to ensure the safe development of language models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how large language models can be trained to generate bad content even when their creators want them to behave safely. This happens when the models are fine-tuned using harmful feedback, which rewards them for producing unsafe text. The researchers find that current safety measures aren’t enough to prevent this from happening. They then test several ways to defend against this vulnerability and discover that none of them work well on their own. This suggests that more research is needed to develop effective defenses. |
Keywords
* Artificial intelligence * Fine tuning * Reinforcement learning
