Summary of Retrieving, Rethinking and Revising: the Chain-of-verification Can Improve Retrieval Augmented Generation, by Bolei He et al.
Retrieving, Rethinking and Revising: The Chain-of-Verification Can Improve Retrieval Augmented Generation
by Bolei He, Nuo Chen, Xinran He, Lingyong Yan, Zhenkai Wei, Jinchang Luo, Zhen-Hua Ling
First submitted to arxiv on: 8 Oct 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 proposed Chain-of-Verification Retrieval Augmented Generation (CoV-RAG) method enhances Large Language Models (LLMs) by incorporating external knowledge while addressing challenges such as incorrect contextual information and inconsistent answers. CoV-RAG integrates a verification module to score, judge, and rewrite generated text. It corrects external retrieval errors by retrieving new knowledge using revised queries and internal generation errors by unifying QA and verification tasks with Chain-of-Thought (CoT) reasoning during training. Experiments across various LLMs demonstrate the effectiveness and adaptability of CoV-RAG compared to strong baselines, surpassing state-of-the-art results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary CoV-RAG is a new way to make Large Language Models better by adding more information from outside sources. Right now, these models can get confused because they might not understand what people are asking or the context of the question. They also might give answers that don’t match what’s being talked about. CoV-RAG fixes this by checking and rewriting the text to make sure it makes sense. It does this by looking at questions and answers together, like a thought process. This helps CoV-RAG learn how to correct mistakes and be more accurate. |
Keywords
» Artificial intelligence » Rag » Retrieval augmented generation
