Hierarchical Retrieval-Augmented Generation Model with Rethink for Multi-hop Question Answering
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866914920534114304 |
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| author | Zhang, Xiaoming Wang, Ming Yang, Xiaocui Wang, Daling Feng, Shi Zhang, Yifei |
| author_facet | Zhang, Xiaoming Wang, Ming Yang, Xiaocui Wang, Daling Feng, Shi Zhang, Yifei |
| contents | Multi-hop Question Answering (QA) necessitates complex reasoning by integrating multiple pieces of information to resolve intricate questions. However, existing QA systems encounter challenges such as outdated information, context window length limitations, and an accuracy-quantity trade-off. To address these issues, we propose a novel framework, the Hierarchical Retrieval-Augmented Generation Model with Rethink (HiRAG), comprising Decomposer, Definer, Retriever, Filter, and Summarizer five key modules. We introduce a new hierarchical retrieval strategy that incorporates both sparse retrieval at the document level and dense retrieval at the chunk level, effectively integrating their strengths. Additionally, we propose a single-candidate retrieval method to mitigate the limitations of multi-candidate retrieval. We also construct two new corpora, Indexed Wikicorpus and Profile Wikicorpus, to address the issues of outdated and insufficient knowledge.
Our experimental results on four datasets demonstrate that HiRAG outperforms state-of-the-art models across most metrics, and our Indexed Wikicorpus is effective. The code for HiRAG is available at https://github.com/2282588541a/HiRAG |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2408_11875 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Hierarchical Retrieval-Augmented Generation Model with Rethink for Multi-hop Question Answering Zhang, Xiaoming Wang, Ming Yang, Xiaocui Wang, Daling Feng, Shi Zhang, Yifei Computation and Language Artificial Intelligence Information Retrieval Multi-hop Question Answering (QA) necessitates complex reasoning by integrating multiple pieces of information to resolve intricate questions. However, existing QA systems encounter challenges such as outdated information, context window length limitations, and an accuracy-quantity trade-off. To address these issues, we propose a novel framework, the Hierarchical Retrieval-Augmented Generation Model with Rethink (HiRAG), comprising Decomposer, Definer, Retriever, Filter, and Summarizer five key modules. We introduce a new hierarchical retrieval strategy that incorporates both sparse retrieval at the document level and dense retrieval at the chunk level, effectively integrating their strengths. Additionally, we propose a single-candidate retrieval method to mitigate the limitations of multi-candidate retrieval. We also construct two new corpora, Indexed Wikicorpus and Profile Wikicorpus, to address the issues of outdated and insufficient knowledge. Our experimental results on four datasets demonstrate that HiRAG outperforms state-of-the-art models across most metrics, and our Indexed Wikicorpus is effective. The code for HiRAG is available at https://github.com/2282588541a/HiRAG |
| title | Hierarchical Retrieval-Augmented Generation Model with Rethink for Multi-hop Question Answering |
| topic | Computation and Language Artificial Intelligence Information Retrieval |
| url | https://arxiv.org/abs/2408.11875 |