Contradiction Detection in RAG Systems: Evaluating LLMs as Context Validators for Improved Information Consistency

2025-03-31

AI Summary

Retrieval-Augmented Generation (RAG) systems often retrieve contradictory documents in dynamic domains (e.g., news), leading large language models (LLMs) to generate inconsistent or erroneous answers. Method: We propose the first multi-type contradiction data generation framework targeting the RAG retrieval stage, and systematically evaluate state-of-the-art LLMs as context-based contradiction verifiers via contradiction synthesis, zero-shot and chain-of-thought (CoT) prompting, and cross-model robustness analysis. Contribution/Results: Our evaluation reveals significant limitations in current SOTA models’ contradiction detection capabilities—performance varies substantially across contradiction types and model scales. CoT prompting exhibits strong model-specificity: it improves accuracy for some models but degrades it for others. The study identifies critical bottlenecks in contradiction awareness within RAG pipelines and establishes a reproducible benchmark and methodological foundation for trustworthy retrieval augmentation.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Language Grounding & Multi-modal NLP
• Multiagent Systems: Adversarial Agents

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

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.

Problem

• Detecting contradictions in RAG systems for information consistency
• Evaluating LLMs as context validators for retrieved documents
• Assessing LLM robustness in handling contradictory information

Innovation

• Novel data generation framework for RAG contradictions
• Evaluating LLMs as context validators for consistency
• Chain-of-thought prompting improves some models