Summary of A Common Pitfall Of Margin-based Language Model Alignment: Gradient Entanglement, by Hui Yuan et al.
A Common Pitfall of Margin-based Language Model Alignment: Gradient Entanglement
by Hui Yuan, Yifan Zeng, Yue Wu, Huazheng Wang, Mengdi Wang, Liu Leqi
First submitted to arxiv on: 17 Oct 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 paper investigates Reinforcement Learning from Human Feedback (RLHF) for language model alignment. Specifically, it highlights the limitations of margin-based methods in specifying ideal language model behavior. The authors identify two unintended consequences: increased probability of dispreferred responses and decreased probability of preferred responses as the margin increases. They attribute this to “gradient entanglement,” where changes in preferred response probabilities are coupled with those of dispreferred responses. The paper theoretically derives conditions under which gradient entanglement becomes concerning and empirically validates its findings. This work has implications for preference optimization algorithms and improving language model alignment. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Reinforcement Learning from Human Feedback (RLHF) helps align language models to human preferences. But, some RLHF methods have a problem: they don’t specify ideal behavior well enough. This can lead to two issues: unsafe responses become more likely, and good responses less likely. The authors explain why this happens due to “gradient entanglement.” They show how this affects different preference optimization algorithms and suggest ways to improve language model alignment. |
Keywords
» Artificial intelligence » Alignment » Language model » Optimization » Probability » Reinforcement learning from human feedback » Rlhf
