Summary of Hiqa: a Hierarchical Contextual Augmentation Rag For Multi-documents Qa, by Xinyue Chen et al.
HiQA: A Hierarchical Contextual Augmentation RAG for Multi-Documents QA
by Xinyue Chen, Pengyu Gao, Jiangjiang Song, Xiaoyang Tan
First submitted to arxiv on: 1 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 presents an advanced question-answering (QA) framework called HiQA that leverages retrieval-augmented generation (RAG) techniques to improve language model accuracy and reliability. By incorporating metadata and a multi-route retrieval mechanism, HiQA enhances the quality of responses while reducing hallucinations. The authors also introduce MasQA, a benchmark dataset for evaluating MDQA performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary HiQA is a new way for computers to answer questions by combining information from multiple sources. This helps make answers more accurate and reliable. Before, some computer models would make up wrong or misleading information. HiQA fixes this problem by using metadata, which is like extra information about the documents being used. It also has a special way of searching through many documents that are similar. |
Keywords
* Artificial intelligence * Language model * Question answering * Rag * Retrieval augmented generation
