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TRACE the Evidence: Constructing Knowledge-Grounded Reasoning Chains for Retrieval-Augmented Generation

A paper proposing TRACE, a method for constructing knowledge-grounded reasoning chains to enhance multi-hop question answering.

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TRACE the Evidence: Constructing Knowledge-Grounded Reasoning Chains for Retrieval-Augmented Generation

By Jinyuan Fang, Zaiqiao Meng, Craig Macdonald
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The authors propose TRACE, a method that constructs knowledge-grounded reasoning chains to improve multi-hop question answering. TRACE uses a KG Generator and Autoregressive Reasoning Chain Constructor to build reasoning chains from retrieved documents.

Experimental results show an average performance improvement of up to 14.03% compared to using all retrieved documents.

Abstract

The authors propose TRACE, a method that constructs knowledge-grounded reasoning chains to improve multi-hop question answering. TRACE uses a KG Generator and Autoregressive Reasoning Chain Constructor to build reasoning chains from retrieved documents. Experimental results show an average performance improvement of up to 14.03% compared to using all retrieved documents.

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question answeringmulti-hop reasoningknowledge-grounded reasoning chainsretrieval-augmented generationnatural language processingKnowledge GraphsLarge Language ModelsRetrieval & RAGSemantic Interoperability
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