Contradiction Detection in RAG Systems: Evaluating LLMs as Context Validators for Improved Information Consistency
This addresses a critical challenge for RAG systems in domains like news, where contradictions can lead to erroneous outputs, but it is incremental as it focuses on evaluating existing methods rather than proposing a new solution.
This study tackled the problem of contradictory information in Retrieval Augmented Generation (RAG) systems by evaluating large language models (LLMs) as context validators, finding that even state-of-the-art LLMs struggle with detection, with performance varying across contradiction types and prompting strategies.
Retrieval Augmented Generation (RAG) systems have emerged as a powerful method for enhancing large language models (LLMs) with up-to-date information. However, the retrieval step in RAG can sometimes surface documents containing contradictory information, particularly in rapidly evolving domains such as news. These contradictions can significantly impact the performance of LLMs, leading to inconsistent or erroneous outputs. This study addresses this critical challenge in two ways. First, we present a novel data generation framework to simulate different types of contradictions that may occur in the retrieval stage of a RAG system. Second, we evaluate the robustness of different LLMs in performing as context validators, assessing their ability to detect contradictory information within retrieved document sets. Our experimental results reveal that context validation remains a challenging task even for state-of-the-art LLMs, with performance varying significantly across different types of contradictions. While larger models generally perform better at contradiction detection, the effectiveness of different prompting strategies varies across tasks and model architectures. We find that chain-of-thought prompting shows notable improvements for some models but may hinder performance in others, highlighting the complexity of the task and the need for more robust approaches to context validation in RAG systems.