通过图谱获取灵感：将合著者图谱与检索增强生成结合用于基于大语言模型的科学想法生成
Graph Your Way to Inspiration: Integrating Co-Author Graphs with Retrieval-Augmented Generation for Large Language Model Based Scientific Idea Generation
AI总结：
本文提出GYWI系统，结合作者图谱与RAG技术，提升大语言模型生成科学想法的可控性和灵感追溯能力。
摘要：
大语言模型（LLMs）在科学想法生成领域展现出潜力。然而，生成的结果往往缺乏可控的学术背景和可追溯的灵感路径。为弥补这一差距，本文提出了一种名为GYWI的科学想法生成系统，该系统结合作者知识图谱与检索增强生成（RAG）以形成外部知识库，为LLMs生成新科学想法提供可控的上下文和灵感路径。我们首先提出了一种以作者为中心的知识图谱构建方法和灵感来源采样算法，以构建外部知识库。然后，我们提出了一种混合检索机制，由RAG和GraphRAG组成，以检索具有深度和广度知识的内容。它形成了混合上下文。第三，我们提出了一种提示优化策略，结合强化学习原理，以自动引导LLMs根据混合上下文优化结果。为了评估所提出的方法，我们基于arXiv（2018-2023）构建了一个评估数据集。本文还开发了一种全面的评估方法，包括多选题任务中的经验自动评估、基于LLM的评分、人工评估和语义空间可视化分析。生成的想法从新颖性、可行性、清晰度、相关性和重要性五个维度进行评估。我们在不同LLMs上进行了实验，包括GPT-4o、DeepSeek-V3、Qwen3-8B和Gemini 2.5。实验结果表明，GYWI在新颖性、可靠性和相关性等多指标上显著优于主流LLMs。
英文摘要：
Large Language Models (LLMs) demonstrate potential in the field of scientific idea generation. However, the generated results often lack controllable academic context and traceable inspiration pathways. To bridge this gap, this paper proposes a scientific idea generation system called GYWI, which combines author knowledge graphs with retrieval-augmented generation (RAG) to form an external knowledge base to provide controllable context and trace of inspiration path for LLMs to generate new scientific ideas. We first propose an author-centered knowledge graph construction method and inspiration source sampling algorithms to construct external knowledge base. Then, we propose a hybrid retrieval mechanism that is composed of both RAG and GraphRAG to retrieve content with both depth and breadth knowledge. It forms a hybrid context. Thirdly, we propose a Prompt optimization strategy incorporating reinforcement learning principles to automatically guide LLMs optimizing the results based on the hybrid context. To evaluate the proposed approaches, we constructed an evaluation dataset based on arXiv (2018-2023). This paper also develops a comprehensive evaluation method including empirical automatic assessment in multiple-choice question task, LLM-based scoring, human evaluation, and semantic space visualization analysis. The generated ideas are evaluated from the following five dimensions: novelty, feasibility, clarity, relevance, and significance. We conducted experiments on different LLMs including GPT-4o, DeepSeek-V3, Qwen3-8B, and Gemini 2.5. Experimental results show that GYWI significantly outperforms mainstream LLMs in multiple metrics such as novelty, reliability, and relevance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
