Thread: A Logic-Based Data Organization Paradigm for How-To Question Answering with Retrieval Augmented Generation

• URL: http://arxiv.org/abs/2406.13372v2
• Date: Thu, 10 Oct 2024 08:04:20 GMT
• Title: Thread: A Logic-Based Data Organization Paradigm for How-To Question Answering with Retrieval Augmented Generation
• Authors: 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,
• Abstract summary: How-to questions are integral to decision-making processes and require dynamic, step-by-step answers. We propose Thread, a novel data organization paradigm aimed at enabling current systems to handle how-to questions more effectively.
• Score: 49.36436704082436
• License:
• Abstract: Recent advances in retrieval-augmented generation have significantly improved the performance of question-answering systems, particularly on factoid '5Ws' questions. However, these systems still face substantial challenges when addressing '1H' questions, specifically how-to questions, which are integral to decision-making processes and require dynamic, step-by-step answers. The key limitation lies in the prevalent data organization paradigm, chunk, which divides documents into fixed-size segments, and disrupts the logical coherence and connections within the context. To overcome this, in this paper, we propose Thread, a novel data organization paradigm aimed at enabling current systems to handle how-to questions more effectively. Specifically, we introduce a new knowledge granularity, termed 'logic unit', where documents are transformed into more structured and loosely interconnected logic units with large language models. Extensive experiments conducted across both open-domain and industrial settings demonstrate that Thread outperforms existing paradigms significantly, improving the success rate of handling how-to questions by 21% to 33%. Moreover, Thread exhibits high adaptability in processing various document formats, drastically reducing the candidate quantity in the knowledge base and minimizing the required information to one-fourth compared with chunk, optimizing both efficiency and effectiveness.

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