Knowledge graph-based thought: a knowledge graph-enhanced LLM framework for pan-cancer question answering.

ABSTRACT:

Background

In recent years, large language models (LLMs) have shown promise in various domains, notably in biomedical sciences. However, their real-world application is often limited by issues like erroneous outputs and hallucinatory responses.

Results

We developed the knowledge graph-based thought (KGT) framework, an innovative solution that integrates LLMs with knowledge graphs (KGs) to improve their initial responses by utilizing verifiable information from KGs, thus significantly reducing factual errors in reasoning. The KGT framework demonstrates strong adaptability and performs well across various open-source LLMs. Notably, KGT can facilitate the discovery of new uses for existing drugs through potential drug-cancer associations and can assist in predicting resistance

SUBMITTER: Feng Y 

PROVIDER: S-EPMC11702363 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature