综述大模型如何辅助科学假说生成，助力科研突破。
A Survey on Hypothesis Generation for Scientific Discovery in the Era of Large Language Models
- 按方法复杂度分类大模型假说生成技术，构建系统性框架
- 提出提升假说新颖性和逻辑性的优化策略
- 适合关注AI辅助科研的学者与跨学科研究者
假说生成是科学发现的核心环节，但正面临信息过载与学科碎片化的挑战。大语言模型（LLMs）的进展为自动化和增强该过程带来新可能。本文全面综述了基于大模型的假说生成研究，包括：(i) 回顾现有方法，从简单提示到复杂框架，并提出一种分类体系；(ii) 分析提升假说质量的技术，如新颖性增强与结构化推理；(iii) 梳理评估策略；(iv) 探讨关键挑战与未来方向，如多模态融合与人机协作。本综述旨在为探索大模型在假说生成中应用的研究者提供参考。
原文摘要 · Abstract (English)
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.
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