Authors: Guangzhi Xiong, Eric Xie, Amir Hassan Shariatmadari, Sikun Guo, Stefan Bekiranov, Aidong Zhang
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.
Source: http://arxiv.org/abs/2411.02382v1
