Summary of Elephant in the Room: Unveiling the Impact Of Reward Model Quality in Alignment, by Yan Liu et al.
Elephant in the Room: Unveiling the Impact of Reward Model Quality in Alignment
by Yan Liu, Xiaoyuan Yi, Xiaokang Chen, Jing Yao, Jingwei Yi, Daoguang Zan, Zheng Liu, Xing Xie, Tsung-Yi Ho
First submitted to arxiv on: 26 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 This paper investigates the quality of reward models used in aligning large language models (LLMs), a crucial aspect often overlooked in previous studies. The authors first curate a clean version of the widely-used preference dataset, HH-RLHF, and then benchmark the accuracy of various reward models used in alignment works. They find that many off-the-shelf reward models are unreliable and should not be used without verification. Furthermore, they show that better reward models perform as better human preference proxies when used for alignment optimization or evaluation. The paper highlights the importance of rigorously evaluating reward models and developing more reliable human proxies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how good the rewards are in making sure big language models behave well. Right now, people are using rewards without checking if they’re any good, which can lead to bad results. The authors make a clean version of one popular set of rewards and test many different reward models to see how good they are. They found that most of them aren’t very reliable. What’s important is choosing the right rewards so that language models behave in a way that’s similar to what humans would want. |
Keywords
» Artificial intelligence » Alignment » Optimization » Rlhf
