Summary of Retrieve, Summarize, Plan: Advancing Multi-hop Question Answering with An Iterative Approach, by Zhouyu Jiang et al.
Retrieve, Summarize, Plan: Advancing Multi-hop Question Answering with an Iterative Approach
by Zhouyu Jiang, Mengshu Sun, Lei Liang, Zhiqiang Zhang
First submitted to arxiv on: 18 Jul 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 iterative Retrieval-Augmented Generation (RAG) method called ReSP to tackle multi-hop question answering. The approach utilizes a dual-function summarizer that compresses information from retrieved documents, targeting both the overarching question and the current sub-question concurrently. By addressing context overload and over-planning issues, ReSP achieves significant performance improvements on HotpotQA and 2WikiMultihopQA datasets, outperforming state-of-the-art methods while demonstrating robustness to varying context lengths. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us answer tricky questions that require searching through lots of information. It’s like using a super-smart helper to find the answers for you. The researchers created a new way called ReSP that uses a special tool to summarize what they find, which makes it better at answering these tough questions. They tested it on two big datasets and found that it works really well! |
Keywords
» Artificial intelligence » Question answering » Rag » Retrieval augmented generation
