PA-RAG: RAG Alignment via Multi-Perspective Preference Optimization

• URL: http://arxiv.org/abs/2412.14510v1
• Date: Thu, 19 Dec 2024 04:18:51 GMT
• Title: PA-RAG: RAG Alignment via Multi-Perspective Preference Optimization
• Authors: Jiayi Wu, Hengyi Cai, Lingyong Yan, Hao Sun, Xiang Li, Shuaiqiang Wang, Dawei Yin, Ming Gao,
• Abstract summary: Retrieval-augmented generation (RAG) has alleviated the issues of outdated and hallucinatory content in large language models (LLMs)<n>RAG generators often suffer from inadequate response informativeness, response robustness, and citation quality.<n>We propose Multiple Perspective Preference Alignment for Retrieval-Augmented Generation (PA-RAG) to align with RAG requirements comprehensively.
• Score: 35.48003039415176
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The emergence of Retrieval-augmented generation (RAG) has alleviated the issues of outdated and hallucinatory content in the generation of large language models (LLMs), yet it still reveals numerous limitations. When a general-purpose LLM serves as the RAG generator, it often suffers from inadequate response informativeness, response robustness, and citation quality. Past approaches to tackle these limitations, either by incorporating additional steps beyond generating responses or optimizing the generator through supervised fine-tuning (SFT), still failed to align with the RAG requirement thoroughly. Consequently, optimizing the RAG generator from multiple preference perspectives while maintaining its end-to-end LLM form remains a challenge. To bridge this gap, we propose Multiple Perspective Preference Alignment for Retrieval-Augmented Generation (PA-RAG), a method for optimizing the generator of RAG systems to align with RAG requirements comprehensively. Specifically, we construct high-quality instruction fine-tuning data and multi-perspective preference data by sampling varied quality responses from the generator across different prompt documents quality scenarios. Subsequently, we optimize the generator using SFT and Direct Preference Optimization (DPO). Extensive experiments conducted on four question-answer datasets across three LLMs demonstrate that PA-RAG can significantly enhance the performance of RAG generators. Our code and datasets are available at https://github.com/wujwyi/PA-RAG.

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