AOP-Smart: A RAG-Enhanced Large Language Model Framework for Adverse Outcome Pathway Analysis

TL;DR

This paper introduces AOP-Smart, a RAG-based framework that enhances large language models' reliability in adverse outcome pathway analysis by retrieving relevant knowledge from AOP-Wiki, significantly reducing hallucinations.

Contribution

The study presents a novel RAG framework tailored for AOP analysis, improving LLM accuracy and consistency in toxicological question answering tasks.

Findings

RAG improves LLM accuracy from 15-35% to over 95%.

AOP-Smart significantly reduces hallucination issues in LLMs.

Experimental results demonstrate enhanced reliability in AOP knowledge tasks.

Abstract

Adverse Outcome Pathways (AOPs) are an important knowledge framework in toxicological research and risk assessment. In recent years, large language models (LLMs) have gradually been applied to AOP-related question answering and mechanistic reasoning tasks. However, due to the existence of the hallucination problem, that is, the model may generate content that is inconsistent with facts or lacks evidence, their reliability is still limited. To address this issue, this study proposes an AOP-oriented Retrieval-Augmented Generation (RAG) framework, AOP-Smart. Based on the official XML data from AOP-Wiki, this method uses Key Events (KEs), Key Event Relationships (KERs), and specific AOP information to retrieve relevant knowledge for user questions, thereby improving the reliability of the generated results of large language models. To evaluate the effectiveness of the proposed method, this study constructed a test set containing 20 AOP-related question answering tasks, covering KE identification, upstream and downstream KE retrieval, and complex AOP retrieval tasks. Experiments were conducted on three mainstream large language models, Gemini, DeepSeek, and ChatGPT, and comparative tests were performed under two settings: without RAG and with RAG. The experimental results show that, without using RAG, the accuracies of GPT, DeepSeek, and Gemini were 15.0\%, 35.0\%, and 20.0\%, respectively; after using RAG, their accuracies increased to 95.0\%, 100.0\%, and 95.0\%, respectively. The results indicate that AOP-Smart can significantly alleviate the hallucination problem of large language models in AOP knowledge tasks, and greatly improve the accuracy and consistency of their answers.