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    <abstract>AI holds promise for transforming scientific processes, including hypothesis generation. Prior work on hypothesis generation can be broadly categorized into theory-driven and data-driven approaches. While both have proven effective in generating novel and plausible hypotheses, it remains an open question whether they can complement each other. To address this, we develop the first method that combines literature-based insights with data to perform LLM-powered hypothesis generation. We apply our method on five different datasets and demonstrate that integrating literature and data outperforms other baselines (8.97% over few-shot, 15.75% over literature-based alone, and 3.37% over data-driven alone). Additionally, we conduct the first human evaluation to assess the utility of LLM-generated hypotheses in assisting human decision-making on two challenging tasks: deception detection and AI generated content detection. Our results show that human accuracy improves significantly by 7.44% and 14.19% on these tasks, respectively. These findings suggest that integrating literature-based and data-driven approaches provides a comprehensive and nuanced framework for hypothesis generation and could open new avenues for scientific inquiry.</abstract>
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%0 Conference Proceedings
%T Literature Meets Data: A Synergistic Approach to Hypothesis Generation
%A Liu, Haokun
%A Zhou, Yangqiaoyu
%A Li, Mingxuan
%A Yuan, Chenfei
%A Tan, Chenhao
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F liu-etal-2025-literature
%X AI holds promise for transforming scientific processes, including hypothesis generation. Prior work on hypothesis generation can be broadly categorized into theory-driven and data-driven approaches. While both have proven effective in generating novel and plausible hypotheses, it remains an open question whether they can complement each other. To address this, we develop the first method that combines literature-based insights with data to perform LLM-powered hypothesis generation. We apply our method on five different datasets and demonstrate that integrating literature and data outperforms other baselines (8.97% over few-shot, 15.75% over literature-based alone, and 3.37% over data-driven alone). Additionally, we conduct the first human evaluation to assess the utility of LLM-generated hypotheses in assisting human decision-making on two challenging tasks: deception detection and AI generated content detection. Our results show that human accuracy improves significantly by 7.44% and 14.19% on these tasks, respectively. These findings suggest that integrating literature-based and data-driven approaches provides a comprehensive and nuanced framework for hypothesis generation and could open new avenues for scientific inquiry.
%R 10.18653/v1/2025.acl-long.12
%U https://aclanthology.org/2025.acl-long.12/
%U https://doi.org/10.18653/v1/2025.acl-long.12
%P 245-281

Markdown (Informal)

[Literature Meets Data: A Synergistic Approach to Hypothesis Generation](https://aclanthology.org/2025.acl-long.12/) (Liu et al., ACL 2025)

• Literature Meets Data: A Synergistic Approach to Hypothesis Generation (Liu et al., ACL 2025)

ACL

• Haokun Liu, Yangqiaoyu Zhou, Mingxuan Li, Chenfei Yuan, and Chenhao Tan. 2025. Literature Meets Data: A Synergistic Approach to Hypothesis Generation. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 245–281, Vienna, Austria. Association for Computational Linguistics.