Large Language Models for Automated Open-domain Scientific Hypotheses Discovery
Zonglin Yang, Xinya Du, Junxian Li, Jie Zheng, Soujanya Poria, Erik Cambria
Abstract
Hypothetical induction is recognized as the main reasoning type when scientists make observations about the world and try to propose hypotheses to explain those observations. Past research on hypothetical induction is under a constrained setting: (1) the observation annotations in the dataset are carefully manually handpicked sentences (resulting in a close-domain setting); and (2) the ground truth hypotheses are mostly commonsense knowledge, making the task less challenging. In this work, we tackle these problems by proposing the first dataset for social science academic hypotheses discovery, with the final goal to create systems that automatically generate valid, novel, and helpful scientific hypotheses, given only a pile of raw web corpus. Unlike previous settings, the new dataset requires (1) using open-domain data (raw web corpus) as observations; and (2) proposing hypotheses even new to humanity. A multi-module framework is developed for the task, including three different feedback mechanisms to boost performance, which exhibits superior performance in terms of both GPT-4 based and expert-based evaluation.To the best of our knowledge, this is the first work showing that LLMs are able to generate novel (”not existing in literature”) and valid (”reflecting reality”) scientific hypotheses.- Anthology ID:
- 2024.findings-acl.804
- Volume:
- Findings of the Association for Computational Linguistics ACL 2024
- Month:
- August
- Year:
- 2024
- Address:
- Bangkok, Thailand and virtual meeting
- Editors:
- Lun-Wei Ku, Andre Martins, Vivek Srikumar
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 13545–13565
- Language:
- URL:
- https://aclanthology.org/2024.findings-acl.804
- DOI:
- Cite (ACL):
- Zonglin Yang, Xinya Du, Junxian Li, Jie Zheng, Soujanya Poria, and Erik Cambria. 2024. Large Language Models for Automated Open-domain Scientific Hypotheses Discovery. In Findings of the Association for Computational Linguistics ACL 2024, pages 13545–13565, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics.
- Cite (Informal):
- Large Language Models for Automated Open-domain Scientific Hypotheses Discovery (Yang et al., Findings 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.findings-acl.804.pdf