Thread: A Logic-Based Data Organization Paradigm for How-To Question Answering with Retrieval Augmented Generation
<|reference_start|>Thread: A Logic-Based Data Organization Paradigm for How-To Question Answering with Retrieval Augmented Generation: 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.<|reference_end|>
@article{an2024thread:,
    title={Thread: A Logic-Based Data Organization Paradigm for How-To Question
  Answering with Retrieval Augmented Generation},
    author={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},
    journal={arXiv preprint arXiv:2406.13372},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.13372},
    primaryClass={cs.AI}
}