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About this paper
Liang Xiao, Wen Dai, Shuai Chen, Bin Qin, Chongyang Shi, Haopeng Jing, Tianyu Guo
RetroRAG proposes a retroactive reasoning paradigm for Retrieval-Augmented Generation (RAG) that enables Large Language Models to iteratively refine their reasoning and evidence, akin to a detective's investigative process. This framework achieved an +8.8 Exact Match and +10.6 F1 improvement on the HotpotQA dataset, reducing hallucinations in multi-hop question answering.
Key insights
- A retroactive reasoning paradigm, allowing LLMs to revisit and revise their reasoning chain based on new evidence, can effectively mitigate errors that propagate in unidirectional RAG systems.
- Dynamic and selective management of evidence, through processes like evidence collation and discovery (ELLERY), is crucial for filtering irrelevant information and preventing 'over-reasoning' caused by excessive retrieved data.
- Ensuring both question-relevance and reference-attribution for inferential evidence significantly enhances the credibility and utility of derived knowledge for complex multi-hop questions.
Summary by alphaXiv.
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