ConflictRAG: Detecting and Resolving Knowledge Conflicts in Retrieval Augmented Generation

ConflictRAG introduces a novel framework for detecting and resolving contradictory information in Retrieval-Augmented Generation systems, achieving 88.7% conflict-detection accuracy while reducing API costs by 62%. The system combines cost-efficient embedding-based detection with selective LLM refinement and demonstrates 5.3-6.1% improvements in answer correctness across multiple benchmarks.

ConflictRAG addresses a fundamental vulnerability in RAG systems that has become increasingly critical as these architectures power production AI applications. Traditional RAG implementations operate under an implicit assumption that retrieved documents are mutually consistent, yet real-world data sources frequently contain contradictory information, outdated facts, and conflicting perspectives. This research tackles that gap by introducing a three-pronged approach: detecting when conflicts exist, classifying their nature, and systematically resolving them before generating responses.

The framework's dual-stage detection mechanism is particularly noteworthy for its pragmatic engineering. By pairing lightweight embedding-based classifiers with selective LLM refinement, the system achieves both cost efficiency and accuracy—reducing API expenditures by 62% while maintaining 90.8% detection rates. This hybrid approach reflects a broader industry trend toward optimizing large language model usage through intelligent triage and staged processing pipelines.

The introduction of Entropy-TOPSIS for credibility assessment moves beyond manual heuristics toward data-driven source ranking, improving accuracy by 7.1%. This methodological shift enables RAG systems to make principled decisions about which sources to prioritize when conflicts arise, rather than defaulting to ordering or arbitrary rules. The new Conflict-Aware RAG Score (CARS) metric provides researchers with diagnostic tools for evaluating how well systems handle conflicting information—a necessary standardization for this emerging challenge.

For the AI development ecosystem, this work has immediate practical implications. Production RAG systems supporting applications in financial services, healthcare, and compliance-sensitive domains cannot afford to generate responses that naively reconcile contradictory sources. The demonstrated cross-LLM transferability suggests ConflictRAG's techniques can integrate into existing deployment pipelines without major architectural changes.

• →ConflictRAG reduces RAG API costs by 62% while maintaining 90.8% conflict-detection accuracy through hybrid embedding-LLM architecture
• →Entropy-TOPSIS framework improves source credibility assessment by 7.1% over manual heuristics for better conflict resolution
• →System achieves 5.3-6.1% correctness improvements and transfers effectively across different backbone LLMs
• →Conflict-Aware RAG Score (CARS) provides new diagnostic metric for evaluating conflict-handling capabilities in generation systems
• →Framework addresses critical real-world vulnerability where retrieved documents contain contradictory information