From Answers to Insights: Unveiling the Strengths and Limitations of ChatGPT and Biomedical Knowledge Graphs

Abstract

Abstract Large Language Models (LLMs) have demonstrated exceptional performance in various natural language processing tasks, utilizing their language generation capabilities and knowledge acquisition potential from unstructured text. However, when applied to the biomedical domain, LLMs encounter limitations, resulting in erroneous and inconsistent answers. Knowledge Graphs (KGs) have emerged as valuable resources for structured information representation and organization. Specifically, Biomedical Knowledge Graphs (BKGs) have attracted significant interest in managing large-scale and heterogeneous biomedical knowledge. This study evaluates the capabilities of ChatGPT and existing BKGs in question answering, knowledge discovery, and reasoning. Results indicate that while ChatGPT with GPT-4.0 surpasses both GPT-3.5 and BKGs in providing existing information, BKGs demonstrate superior information reliability. Additionally, ChatGPT exhibits limitations in performing novel discoveries and reasoning, particularly in establishing structured links between entities compared to BKGs. To overcome these limitations, future research should focus on integrating LLMs and BKGs to leverage their respective strengths. Such an integrated approach would optimize task performance and mitigate potential risks, thereby advancing knowledge in the biomedical field and contributing to overall well-being.

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