Summary of Data-centric Human Preference Optimization with Rationales, by Hoang Anh Just et al.
Data-Centric Human Preference Optimization with Rationales
by Hoang Anh Just, Ming Jin, Anit Sahu, Huy Phan, Ruoxi Jia
First submitted to arxiv on: 19 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 research paper proposes a novel approach to improving reinforcement learning from human feedback in language models. By enriching existing preference datasets with machine-generated rationales that explain the reasons behind choices, the authors develop a simple and principled framework for augmenting current preference learning methods. The study highlights how rationales enhance learning efficiency, accelerating convergence to higher-performing models and reducing verbosity bias and hallucination. Experiments demonstrate the advantages of rationale-enriched preference learning, including improved data efficiency and versatility in integrating with various preference optimization algorithms. This research has significant implications for re-imagining data design for preference learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps language models learn from people’s preferences better. Right now, we compare responses to see what works best. But this study shows that adding reasons why we chose certain answers can make a big difference. The researchers created a new way to add these reasons (called rationales) to the data and tested it on several tasks. They found that using rationales makes learning more efficient, gets better results faster, and reduces mistakes. This is important because language models need to learn what people like and dislike to improve. |
Keywords
» Artificial intelligence » Hallucination » Optimization » Reinforcement learning from human feedback
