用于科学思想生成的大型语言模型：以创造力为中心的综述
Large Language Models for Scientific Idea Generation: A Creativity-Centered Survey
AI总结：
本文综述了大型语言模型在科学思想生成中的应用，分析了不同方法在创新性与科学合理性之间的权衡，并提出了评估框架以指导未来研究方向。
摘要：
科学思想生成是发现的核心，需要同时满足新颖性和科学合理性。与标准推理或一般创造性生成不同，科学构思本质上是开放性的和多目标的，使其自动化特别具有挑战性。近年来，大型语言模型（LLMs）的进步使生成连贯且合理的科学思想成为可能，但它们的创造性能力的本质和限制仍不为人知。本文综述提供了对LLM驱动的科学构思方法的结构化综合，重点在于不同方法如何在新颖性和科学有效性之间进行权衡。我们将现有方法分为五个互补的类别：外部知识增强、基于提示的分布引导、推理时间扩展、多代理协作和参数级适应。为了解释其贡献，我们采用了两个互补的创造力框架：Boden分类法用于表征预期的创造性新颖性水平，以及Rhodes 4Ps框架用于分析每种方法强调的创造力方面或来源。通过将方法学发展与认知创造力框架对齐，本文澄清了评估景观，并确定了可靠和系统的LLM驱动科学发现的关键挑战和方向。
英文摘要：
Scientific idea generation is central to discovery, requiring the joint satisfaction of novelty and scientific soundness. Unlike standard reasoning or general creative generation, scientific ideation is inherently open-ended and multi-objective, making its automation particularly challenging. Recent advances in large language models (LLMs) have enabled the generation of coherent and plausible scientific ideas, yet the nature and limits of their creative capabilities remain poorly understood. This survey provides a structured synthesis of methods for LLM-driven scientific ideation, focusing on how different approaches trade off novelty and scientific validity. We organize existing methods into five complementary families: External knowledge augmentation, Prompt-based distributional steering, Inference-time scaling, Multi-agent collaboration, and Parameter-level adaptation. To interpret their contributions, we adopt two complementary creativity frameworks: Boden taxonomy to characterize the expected level of creative novelty, and Rhodes 4Ps framework to analyze the aspects or sources of creativity emphasized by each method. By aligning methodological developments with cognitive creativity frameworks, this survey clarifies the evaluation landscape and identifies key challenges and directions for reliable and systematic LLM-based scientific discovery.
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