Scientific Hypothesis Generation and Validation: Methods, Datasets, and Future Directions
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866916725870559232 |
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| author | Kulkarni, Adithya Alotaibi, Fatimah Zeng, Xinyue Wu, Longfeng Zeng, Tong Yao, Barry Menglong Liu, Minqian Zhang, Shuaicheng Huang, Lifu Zhou, Dawei |
| author_facet | Kulkarni, Adithya Alotaibi, Fatimah Zeng, Xinyue Wu, Longfeng Zeng, Tong Yao, Barry Menglong Liu, Minqian Zhang, Shuaicheng Huang, Lifu Zhou, Dawei |
| contents | Large Language Models (LLMs) are transforming scientific hypothesis generation and validation by enabling information synthesis, latent relationship discovery, and reasoning augmentation. This survey provides a structured overview of LLM-driven approaches, including symbolic frameworks, generative models, hybrid systems, and multi-agent architectures. We examine techniques such as retrieval-augmented generation, knowledge-graph completion, simulation, causal inference, and tool-assisted reasoning, highlighting trade-offs in interpretability, novelty, and domain alignment. We contrast early symbolic discovery systems (e.g., BACON, KEKADA) with modern LLM pipelines that leverage in-context learning and domain adaptation via fine-tuning, retrieval, and symbolic grounding. For validation, we review simulation, human-AI collaboration, causal modeling, and uncertainty quantification, emphasizing iterative assessment in open-world contexts. The survey maps datasets across biomedicine, materials science, environmental science, and social science, introducing new resources like AHTech and CSKG-600. Finally, we outline a roadmap emphasizing novelty-aware generation, multimodal-symbolic integration, human-in-the-loop systems, and ethical safeguards, positioning LLMs as agents for principled, scalable scientific discovery. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_04651 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Scientific Hypothesis Generation and Validation: Methods, Datasets, and Future Directions Kulkarni, Adithya Alotaibi, Fatimah Zeng, Xinyue Wu, Longfeng Zeng, Tong Yao, Barry Menglong Liu, Minqian Zhang, Shuaicheng Huang, Lifu Zhou, Dawei Computation and Language Machine Learning Large Language Models (LLMs) are transforming scientific hypothesis generation and validation by enabling information synthesis, latent relationship discovery, and reasoning augmentation. This survey provides a structured overview of LLM-driven approaches, including symbolic frameworks, generative models, hybrid systems, and multi-agent architectures. We examine techniques such as retrieval-augmented generation, knowledge-graph completion, simulation, causal inference, and tool-assisted reasoning, highlighting trade-offs in interpretability, novelty, and domain alignment. We contrast early symbolic discovery systems (e.g., BACON, KEKADA) with modern LLM pipelines that leverage in-context learning and domain adaptation via fine-tuning, retrieval, and symbolic grounding. For validation, we review simulation, human-AI collaboration, causal modeling, and uncertainty quantification, emphasizing iterative assessment in open-world contexts. The survey maps datasets across biomedicine, materials science, environmental science, and social science, introducing new resources like AHTech and CSKG-600. Finally, we outline a roadmap emphasizing novelty-aware generation, multimodal-symbolic integration, human-in-the-loop systems, and ethical safeguards, positioning LLMs as agents for principled, scalable scientific discovery. |
| title | Scientific Hypothesis Generation and Validation: Methods, Datasets, and Future Directions |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2505.04651 |