Summary of Pa-rag: Rag Alignment Via Multi-perspective Preference Optimization, by Jiayi Wu et al.
PA-RAG: RAG Alignment via Multi-Perspective Preference Optimization
by Jiayi Wu, Hengyi Cai, Lingyong Yan, Hao Sun, Xiang Li, Shuaiqiang Wang, Dawei Yin, Ming Gao
First submitted to arxiv on: 19 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 addresses limitations in Retrieval-augmented generation (RAG) of large language models (LLMs), which still produce outdated and hallucinatory content. RAG generators often suffer from inadequate response informativeness, robustness, and citation quality when using general-purpose LLMs. The authors propose Multiple Perspective Preference Alignment for Retrieval-Augmented Generation (PA-RAG) to optimize the generator, aligning with RAG requirements. This is achieved by constructing high-quality instruction fine-tuning data and multi-perspective preference data, and optimizing the generator through supervised fine-tuning (SFT) and Direct Preference Optimization (DPO). The method is evaluated on four question-answer datasets across three LLMs, demonstrating significant performance enhancements. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper tries to make large language models better by fixing some problems they have. These models are good at generating text, but sometimes they produce old or made-up information. To fix this, the authors suggest a new way of training these models called PA-RAG. It works by creating special data and using it to teach the model what’s good and what’s not. They tested their method on some datasets and found that it really helps make the language models better. |
Keywords
» Artificial intelligence » Alignment » Fine tuning » Optimization » Rag » Retrieval augmented generation » Supervised
