Summary of Trace the Evidence: Constructing Knowledge-grounded Reasoning Chains For Retrieval-augmented Generation, by Jinyuan Fang et al.
TRACE the Evidence: Constructing Knowledge-Grounded Reasoning Chains for Retrieval-Augmented Generation
by Jinyuan Fang, Zaiqiao Meng, Craig Macdonald
First submitted to arxiv on: 17 Jun 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 This paper proposes a novel approach to enhance the multi-hop reasoning ability of retrieval-augmented generation (RAG) models for question answering tasks. The proposed method, called TRACE, constructs knowledge-grounded reasoning chains to identify and integrate supporting evidence from retrieved documents. This is achieved by generating a knowledge graph (KG) from the retrieved documents using a KG Generator and then constructing reasoning chains with an Autoregressive Reasoning Chain Constructor. Experimental results on three multi-hop QA datasets show that TRACE outperforms traditional RAG models, achieving an average performance improvement of up to 14.03%. The results also suggest that using reasoning chains as context is often sufficient for correctly answering questions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research makes it easier for computers to answer tricky questions by creating a special kind of chain that helps them understand the information they find on the internet. This method, called TRACE, uses information from the internet and combines it in a smart way to get better answers. The researchers tested this approach on many difficult questions and found that it worked much better than other methods, making it more accurate by up to 14%. This is important because it helps computers understand complex ideas and provide better answers. |
Keywords
» Artificial intelligence » Autoregressive » Knowledge graph » Question answering » Rag » Retrieval augmented generation
