Abstract
Large language models (LLMs) have demonstrated remarkable capabilities in various scientific domains, from natural language processing to complex problem-solving tasks. Their ability to understand and generate human-like text has opened up new possibilities for advancing scientific research, enabling tasks such as data analysis, literature review, and even experimental design. One of the most promising applications of LLMs in this context is hypothesis generation, where they can identify novel research directions by analyzing existing knowledge. However, despite their potential, LLMs are prone to generating ``hallucinations'', outputs that are plausible-sounding but factually incorrect. Such a problem presents significant challenges in scientific fields that demand rigorous accuracy and verifiability, potentially leading to erroneous or misleading conclusions. To overcome these challenges, we propose KG-CoI (Knowledge Grounded Chain of Ideas), a novel system that enhances LLM hypothesis generation by integrating external, structured knowledge from knowledge graphs (KGs). KG-CoI guides LLMs through a structured reasoning process, organizing their output as a chain of ideas (CoI), and includes a KG-supported module for the detection of hallucinations. With experiments on our newly constructed hypothesis generation dataset, we demonstrate that KG-CoI not only improves the accuracy of LLM-generated hypotheses but also reduces the hallucination in their reasoning chains, highlighting its effectiveness in advancing real-world scientific research.
Abstract (translated)
大型语言模型（LLMs）在多个科学领域展示了显著的能力，从自然语言处理到复杂问题解决任务。它们理解和生成类人文本的能力为推进科学研究开辟了新的可能性，使数据解析、文献回顾乃至实验设计等任务成为可能。在这种背景下，LLMs 最有前景的应用之一是假设生成，通过分析现有知识，它们可以识别出新颖的研究方向。然而，尽管有这些潜力，LLMs 容易产生“幻觉”，即听起来合理但实际上错误的输出。这样的问题在要求严格准确性和可验证性的科学领域中构成了重大挑战，可能导致错误或误导性的结论。为克服这些挑战，我们提出了KG-CoI（知识基础的想法链），这是一个通过集成来自知识图谱（KGs）的外部结构化知识来增强LLM假设生成能力的新系统。KG-CoI 引导 LLM 通过一个有组织的推理过程，并将它们的输出组织成一条想法链（CoI）。此外，它还包括一个基于KG的支持模块用于检测幻觉。通过对新构建的假设生成数据集进行实验，我们展示了KG-CoI不仅提高了LLM生成假设的准确性，还减少了其推理链条中的幻觉现象，突显了其在促进现实世界科学研究方面的有效性。
URL
https://arxiv.org/abs/2411.02382
