A Survey on Hypothesis Generation for Scientific Discovery in the Era of Large Language Models
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arXiv
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| Main Authors: | , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866917979706359808 |
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| author | Alkan, Atilla Kaan Sourav, Shashwat Jablonska, Maja Astarita, Simone Chakrabarty, Rishabh Garuda, Nikhil Khetarpal, Pranav Pióro, Maciej Tanoglidis, Dimitrios Iyer, Kartheik G. Polimera, Mugdha S. Smith, Michael J. Ghosal, Tirthankar Huertas-Company, Marc Kruk, Sandor Schawinski, Kevin Ciucă, Ioana |
| author_facet | Alkan, Atilla Kaan Sourav, Shashwat Jablonska, Maja Astarita, Simone Chakrabarty, Rishabh Garuda, Nikhil Khetarpal, Pranav Pióro, Maciej Tanoglidis, Dimitrios Iyer, Kartheik G. Polimera, Mugdha S. Smith, Michael J. Ghosal, Tirthankar Huertas-Company, Marc Kruk, Sandor Schawinski, Kevin Ciucă, Ioana |
| contents | Hypothesis generation is a fundamental step in scientific discovery, yet it is increasingly challenged by information overload and disciplinary fragmentation. Recent advances in Large Language Models (LLMs) have sparked growing interest in their potential to enhance and automate this process. This paper presents a comprehensive survey of hypothesis generation with LLMs by (i) reviewing existing methods, from simple prompting techniques to more complex frameworks, and proposing a taxonomy that categorizes these approaches; (ii) analyzing techniques for improving hypothesis quality, such as novelty boosting and structured reasoning; (iii) providing an overview of evaluation strategies; and (iv) discussing key challenges and future directions, including multimodal integration and human-AI collaboration. Our survey aims to serve as a reference for researchers exploring LLMs for hypothesis generation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_05496 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Survey on Hypothesis Generation for Scientific Discovery in the Era of Large Language Models Alkan, Atilla Kaan Sourav, Shashwat Jablonska, Maja Astarita, Simone Chakrabarty, Rishabh Garuda, Nikhil Khetarpal, Pranav Pióro, Maciej Tanoglidis, Dimitrios Iyer, Kartheik G. Polimera, Mugdha S. Smith, Michael J. Ghosal, Tirthankar Huertas-Company, Marc Kruk, Sandor Schawinski, Kevin Ciucă, Ioana Computation and Language 68T50 Hypothesis generation is a fundamental step in scientific discovery, yet it is increasingly challenged by information overload and disciplinary fragmentation. Recent advances in Large Language Models (LLMs) have sparked growing interest in their potential to enhance and automate this process. This paper presents a comprehensive survey of hypothesis generation with LLMs by (i) reviewing existing methods, from simple prompting techniques to more complex frameworks, and proposing a taxonomy that categorizes these approaches; (ii) analyzing techniques for improving hypothesis quality, such as novelty boosting and structured reasoning; (iii) providing an overview of evaluation strategies; and (iv) discussing key challenges and future directions, including multimodal integration and human-AI collaboration. Our survey aims to serve as a reference for researchers exploring LLMs for hypothesis generation. |
| title | A Survey on Hypothesis Generation for Scientific Discovery in the Era of Large Language Models |
| topic | Computation and Language 68T50 |
| url | https://arxiv.org/abs/2504.05496 |