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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2406.13372 |
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| _version_ | 1866909811038224384 |
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| author | An, Kaikai Yang, Fangkai Li, Liqun Lu, Junting Cheng, Sitao Si, Shuzheng Wang, Lu Zhao, Pu Cao, Lele Lin, Qingwei Rajmohan, Saravan Zhang, Dongmei Chang, Baobao |
| author_facet | An, Kaikai Yang, Fangkai Li, Liqun Lu, Junting Cheng, Sitao Si, Shuzheng Wang, Lu Zhao, Pu Cao, Lele Lin, Qingwei Rajmohan, Saravan Zhang, Dongmei Chang, Baobao |
| contents | Recent advances in retrieval-augmented generation (RAG) have substantially improved question-answering systems, particularly for factoid '5Ws' questions. However, significant challenges remain when addressing '1H' questions, specifically how-to questions, which are integral for decision-making and require dynamic, step-by-step responses. The key limitation lies in the prevalent data organization paradigm, chunk, which commonly divides documents into fixed-size segments, and disrupts the logical coherence and connections within the context. To address this, we propose Thread, a novel data organization paradigm enabling systems to handle how-to questions more effectively. Specifically, we introduce a new knowledge granularity, 'logic unit' (LU), where large language models transform documents into more structured and loosely interconnected LUs. Extensive experiments across both open-domain and industrial settings show that Thread outperforms existing paradigms significantly, improving the success rate of handling how-to questions by 21% to 33%. Additionally, Thread demonstrates high adaptability across diverse document formats, reducing retrieval information by up to 75% compared to chunk, and also shows better generalizability to '5Ws' questions, such as multi-hop questions, outperforming other paradigms. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_13372 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Thread: A Logic-Based Data Organization Paradigm for How-To Question Answering with Retrieval Augmented Generation An, Kaikai Yang, Fangkai Li, Liqun Lu, Junting Cheng, Sitao Si, Shuzheng Wang, Lu Zhao, Pu Cao, Lele Lin, Qingwei Rajmohan, Saravan Zhang, Dongmei Chang, Baobao Artificial Intelligence Recent advances in retrieval-augmented generation (RAG) have substantially improved question-answering systems, particularly for factoid '5Ws' questions. However, significant challenges remain when addressing '1H' questions, specifically how-to questions, which are integral for decision-making and require dynamic, step-by-step responses. The key limitation lies in the prevalent data organization paradigm, chunk, which commonly divides documents into fixed-size segments, and disrupts the logical coherence and connections within the context. To address this, we propose Thread, a novel data organization paradigm enabling systems to handle how-to questions more effectively. Specifically, we introduce a new knowledge granularity, 'logic unit' (LU), where large language models transform documents into more structured and loosely interconnected LUs. Extensive experiments across both open-domain and industrial settings show that Thread outperforms existing paradigms significantly, improving the success rate of handling how-to questions by 21% to 33%. Additionally, Thread demonstrates high adaptability across diverse document formats, reducing retrieval information by up to 75% compared to chunk, and also shows better generalizability to '5Ws' questions, such as multi-hop questions, outperforming other paradigms. |
| title | Thread: A Logic-Based Data Organization Paradigm for How-To Question Answering with Retrieval Augmented Generation |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2406.13372 |