Summary of Hierarchical Retrieval-augmented Generation Model with Rethink For Multi-hop Question Answering, by Xiaoming Zhang et al.
Hierarchical Retrieval-Augmented Generation Model with Rethink for Multi-hop Question Answering
by Xiaoming Zhang, Ming Wang, Xiaocui Yang, Daling Wang, Shi Feng, Yifei Zhang
First submitted to arxiv on: 20 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
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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 Hierarchical Retrieval-Augmented Generation Model with Rethink (HiRAG) addresses challenges in Multi-hop Question Answering (QA), including outdated information, context window length limitations, and an accuracy-quantity trade-off. HiRAG comprises five key modules: Decomposer, Definer, Retriever, Filter, and Summarizer. A novel hierarchical retrieval strategy combines sparse and dense retrieval at the document and chunk levels, respectively. Additionally, a single-candidate retrieval method is proposed to mitigate limitations of multi-candidate retrieval. The model is evaluated using two new corpora: Indexed Wikicorpus and Profile Wikicorpus. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary HiRAG is a new way to answer complex questions by combining multiple pieces of information. It’s like having a superpower that can understand and connect lots of different facts to give you the right answer. The model has five main parts: Decomposer, Definer, Retriever, Filter, and Summarizer. HiRAG also uses a new way to find relevant information by combining two types of searching: finding the best document and finding the best chunk within that document. This helps to overcome some limitations of previous methods. To test this model, the authors created two new databases: Indexed Wikicorpus and Profile Wikicorpus. |
Keywords
» Artificial intelligence » Context window » Question answering » Retrieval augmented generation
