Summary of Backtracking Improves Generation Safety, by Yiming Zhang et al.
Backtracking Improves Generation Safety
by Yiming Zhang, Jianfeng Chi, Hailey Nguyen, Kartikeya Upasani, Daniel M. Bikel, Jason Weston, Eric Michael Smith
First submitted to arxiv on: 22 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 backtracking technique allows language models to “undo” and recover from their own unsafe generation through the introduction of a special [RESET] token. This deviates from the paradigm of approaching safety alignment as prevention, instead decreasing the probability of harmful responses. The method can be incorporated into either SFT or DPO training to optimize helpfulness and harmlessness. Compared to baseline models, backtracking Llama-3-8B is four times more safe in our evaluations without regression in helpfulness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary When language models generate text, they often keep going even if the output isn’t good. This can be a problem because it means they might produce unsafe or harmful content. To fix this, researchers propose a new technique called backtracking. It’s like having an “undo” button for language models that lets them start over if their initial response is bad. The goal is to make sure language models are helpful and safe, while also being able to correct themselves when they make mistakes. |
Keywords
» Artificial intelligence » Alignment » Llama » Probability » Regression » Token
