Summary of Rethinking Bradley-terry Models in Preference-based Reward Modeling: Foundations, Theory, and Alternatives, by Hao Sun et al.
Rethinking Bradley-Terry Models in Preference-Based Reward Modeling: Foundations, Theory, and Alternatives
by Hao Sun, Yunyi Shen, Jean-Francois Ton
First submitted to arxiv on: 7 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 explores the Bradley-Terry (BT) model’s applications in reward modeling for Large Language Model (LLM) alignment. The BT model is commonly used to convert pairwise response comparisons into reward values and make predictions. However, it remains unclear why this model can be adopted from its original use in multi-player stochastic game matching. This study establishes the convergence rate of BT reward models based on deep neural networks using embeddings, providing a theoretical foundation for their use. The paper also argues that the BT model is not the only necessary choice and proposes an alternative order-consistent reward modeling objective. Empirical evaluations across 12,000 experimental setups using six base LLMs, two datasets, and diverse annotation designs are presented. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study investigates why the Bradley-Terry (BT) model can be used in reward modeling for Large Language Models (LLMs). The BT model was originally designed for multi-player games, but it’s also used to convert pairwise responses into rewards. This paper looks at why this works and proposes a new way of doing things. It shows that the BT model is not the only option and that there are other ways to get good results. The study tested different methods across many experiments using different LLMs, datasets, and ways of labeling data. |
Keywords
» Artificial intelligence » Alignment » Large language model
