Summary of Towards Data-centric Rlhf: Simple Metrics For Preference Dataset Comparison, by Judy Hanwen Shen et al.
Towards Data-Centric RLHF: Simple Metrics for Preference Dataset Comparison
by Judy Hanwen Shen, Archit Sharma, Jun Qin
First submitted to arxiv on: 15 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Machine Learning (cs.LG)
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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 proposes a systematic study of publicly available preference datasets used for training reward models in reinforcement learning from human feedback (RLHF). It aims to compare these datasets through three perspectives: scale, label noise, and information content. The authors develop specific metrics for each perspective and provide insights that can aid in training efficiency and iterative data collection for RLHF. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper compares different preference datasets used in training reward models. It looks at how big the datasets are, how noisy the labels are, and what kind of information they contain. The goal is to help people choose the right dataset for their task and make it easier to collect more data if needed. This can improve the way machines learn from humans. |
Keywords
» Artificial intelligence » Reinforcement learning from human feedback » Rlhf
