Summary of Rethinking the Role Of Proxy Rewards in Language Model Alignment, by Sungdong Kim and Minjoon Seo
Rethinking the Role of Proxy Rewards in Language Model Alignment
by Sungdong Kim, Minjoon Seo
First submitted to arxiv on: 2 Feb 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 the role of proxy rewards in aligning Large Language Models (LLMs) with human values. Specifically, it proposes a “reverse reward engineering” approach, which composes interpretable features as a white-box reward function to replicate the ground truth (gold) reward signal. The study finds that successfully emulating the gold reward requires generating responses that are relevant and consistent for both open-ended and closed-ended questions. Furthermore, models optimized using this devised white-box reward demonstrate competitive performances with strong open-source RMs in alignment benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are trying to be more like humans by learning from feedback. But, they don’t always understand what we mean. This paper helps fix that problem by creating a new way to give them rewards that align with what humans want. They show that it’s possible to make the model generate answers that are relevant and consistent, without needing lots of human feedback. The result is a strong reward baseline that can be used for alignment benchmarks. |
Keywords
* Artificial intelligence * Alignment
