Summary of Relative Preference Optimization: Enhancing Llm Alignment Through Contrasting Responses Across Identical and Diverse Prompts, by Yueqin Yin et al.
Relative Preference Optimization: Enhancing LLM Alignment through Contrasting Responses across Identical and Diverse Prompts
by Yueqin Yin, Zhendong Wang, Yi Gu, Hai Huang, Weizhu Chen, Mingyuan Zhou
First submitted to arxiv on: 12 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 This paper addresses the challenge of aligning large language models (LLMs) with diverse user preferences. The existing Direct Preference Optimization (DPO) method has limitations, as it only considers paired preferences and neglects the complexities of human learning, which often involves understanding contrasting responses to similar questions. To overcome this shortfall, the authors propose Relative Preference Optimization (RPO), a novel approach that introduces a contrastive weighting mechanism to leverage a broader range of preference data, including both paired and unpaired sets. This enables LLMs to better align with user preferences and improve their adaptability during training. The proposed method is evaluated through empirical tests on dialogue and summarization tasks, achieving superior results compared to DPO. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how language models can be trained to better match what people like or dislike. Right now, these models are only as good as the preferences we give them, which can be limited. The researchers want to make it easier for language models to learn from a wider range of preferences, including ones that might not be directly comparable. They propose a new method called Relative Preference Optimization (RPO) that does just that. By using RPO, language models can become better at understanding what people prefer and adapt to different situations. |
Keywords
* Artificial intelligence * Optimization * Summarization
