Summary of Thread: a Logic-based Data Organization Paradigm For How-to Question Answering with Retrieval Augmented Generation, by Kaikai An et al.
Thread: A Logic-Based Data Organization Paradigm for How-To Question Answering with Retrieval Augmented Generation
by Kaikai An, Fangkai Yang, Liqun Li, Junting Lu, Sitao Cheng, Shuzheng Si, Lu Wang, Pu Zhao, Lele Cao, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang, Qi Zhang, Baobao Chang
First submitted to arxiv on: 19 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 research paper introduces a novel data organization paradigm called Thread, aimed at enhancing the performance of question-answering systems, particularly for handling how-to questions. The current retrieval-augmented generation approach has improved significantly on factoid ‘5Ws’ questions but struggles with dynamic, step-by-step answers required for decision-making processes. The paper’s key contribution lies in introducing a new knowledge granularity, termed ‘logic unit’, which transforms documents into more structured and loosely interconnected logic units using large language models. Experimental results across open-domain and industrial settings demonstrate Thread outperforms existing paradigms by 21% to 33% in handling how-to questions. The proposed approach also exhibits high adaptability in processing various document formats, reducing the required information by one-fourth compared to chunk. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The research paper proposes a new way of organizing data called Thread, which helps question-answering systems answer more complex “how-to” questions. Currently, these systems are great at answering simple “who”, “what”, and “when” questions but struggle with step-by-step instructions needed for making decisions. The team’s solution is to divide documents into smaller, connected chunks that make sense together. This approach, called Thread, performs much better than existing methods in handling how-to questions, with a significant improvement of 21% to 33%. Additionally, Thread can handle different types of documents and reduces the amount of information needed by one-fourth. |
Keywords
» Artificial intelligence » Question answering » Retrieval augmented generation
