Summary of Sirerag: Indexing Similar and Related Information For Multihop Reasoning, by Nan Zhang et al.
SiReRAG: Indexing Similar and Related Information for Multihop Reasoning
by Nan Zhang, Prafulla Kumar Choubey, Alexander Fabbri, Gabriel Bernadett-Shapiro, Rui Zhang, Prasenjit Mitra, Caiming Xiong, Chien-Sheng Wu
First submitted to arxiv on: 9 Dec 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 SiReRAG approach for retrieval-augmented generation (RAG) systems explicitly considers both semantic similarity and related information, leading to improved performance on complex tasks requiring multihop reasoning. The method constructs a unified retrieval pool by indexing and flattening similarity and relatedness trees, which are built using recursive summarization and proposition/entity grouping, respectively. Experiments demonstrate that SiReRAG outperforms state-of-the-art indexing methods on three multihop datasets, with an average 1.9% improvement in F1 scores. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary SiReRAG is a new way to help computers generate text by finding the right information in big databases. Right now, these systems only look at how similar two pieces of text are or what’s related to each other. But that’s not enough for complex tasks like answering questions that require understanding multiple pieces of information. Our approach, SiReRAG, looks at both similarity and relatedness, making it better at finding the right answers. We tested SiReRAG on three big datasets and found that it works much better than current methods, with an average improvement of 1.9%. This could help computers generate more accurate text in the future. |
Keywords
» Artificial intelligence » Rag » Retrieval augmented generation » Summarization
