Improving Scientific Document Retrieval with Concept Coverage-based Query Set Generation
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866915154254364672 |
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| author | Kang, SeongKu Jin, Bowen Kweon, Wonbin Zhang, Yu Lee, Dongha Han, Jiawei Yu, Hwanjo |
| author_facet | Kang, SeongKu Jin, Bowen Kweon, Wonbin Zhang, Yu Lee, Dongha Han, Jiawei Yu, Hwanjo |
| contents | In specialized fields like the scientific domain, constructing large-scale human-annotated datasets poses a significant challenge due to the need for domain expertise. Recent methods have employed large language models to generate synthetic queries, which serve as proxies for actual user queries. However, they lack control over the content generated, often resulting in incomplete coverage of academic concepts in documents. We introduce Concept Coverage-based Query set Generation (CCQGen) framework, designed to generate a set of queries with comprehensive coverage of the document's concepts. A key distinction of CCQGen is that it adaptively adjusts the generation process based on the previously generated queries. We identify concepts not sufficiently covered by previous queries, and leverage them as conditions for subsequent query generation. This approach guides each new query to complement the previous ones, aiding in a thorough understanding of the document. Extensive experiments demonstrate that CCQGen significantly enhances query quality and retrieval performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_11181 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Improving Scientific Document Retrieval with Concept Coverage-based Query Set Generation Kang, SeongKu Jin, Bowen Kweon, Wonbin Zhang, Yu Lee, Dongha Han, Jiawei Yu, Hwanjo Information Retrieval Artificial Intelligence In specialized fields like the scientific domain, constructing large-scale human-annotated datasets poses a significant challenge due to the need for domain expertise. Recent methods have employed large language models to generate synthetic queries, which serve as proxies for actual user queries. However, they lack control over the content generated, often resulting in incomplete coverage of academic concepts in documents. We introduce Concept Coverage-based Query set Generation (CCQGen) framework, designed to generate a set of queries with comprehensive coverage of the document's concepts. A key distinction of CCQGen is that it adaptively adjusts the generation process based on the previously generated queries. We identify concepts not sufficiently covered by previous queries, and leverage them as conditions for subsequent query generation. This approach guides each new query to complement the previous ones, aiding in a thorough understanding of the document. Extensive experiments demonstrate that CCQGen significantly enhances query quality and retrieval performance. |
| title | Improving Scientific Document Retrieval with Concept Coverage-based Query Set Generation |
| topic | Information Retrieval Artificial Intelligence |
| url | https://arxiv.org/abs/2502.11181 |