Retrieval-Augmented Generation by Evidence Retroactivity in LLMs

• URL: http://arxiv.org/abs/2501.05475v1
• Date: Tue, 07 Jan 2025 08:57:42 GMT
• Title: Retrieval-Augmented Generation by Evidence Retroactivity in LLMs
• Authors: Liang Xiao, Wen Dai, Shuai Chen, Bin Qin, Chongyang Shi, Haopeng Jing, Tianyu Guo,
• Abstract summary: Retroactive Retrieval-Augmented Generation (RetroRAG) is a novel framework to build a retroactive reasoning paradigm. RetroRAG revises and updates the evidence, redirecting the reasoning chain to the correct direction. Empirical evaluations show that RetroRAG significantly outperforms existing methods.
• Score: 19.122314663040726
• License:
• Abstract: Retrieval-augmented generation has gained significant attention due to its ability to integrate relevant external knowledge, enhancing the accuracy and reliability of the LLMs' responses. Most of the existing methods apply a dynamic multiple retrieval-generating process, to address multi-hop complex questions by decomposing them into sub-problems. However, these methods rely on an unidirectional forward reasoning paradigm, where errors from insufficient reasoning steps or inherent flaws in current retrieval systems are irreversible, potentially derailing the entire reasoning chain. For the first time, this work introduces Retroactive Retrieval-Augmented Generation (RetroRAG), a novel framework to build a retroactive reasoning paradigm. RetroRAG revises and updates the evidence, redirecting the reasoning chain to the correct direction. RetroRAG constructs an evidence-collation-discovery framework to search, generate, and refine credible evidence. It synthesizes inferential evidence related to the key entities in the question from the existing source knowledge and formulates search queries to uncover additional information. As new evidence is found, RetroRAG continually updates and organizes this information, enhancing its ability to locate further necessary evidence. Paired with an Answerer to generate and evaluate outputs, RetroRAG is capable of refining its reasoning process iteratively until a reliable answer is obtained. Empirical evaluations show that RetroRAG significantly outperforms existing methods.

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