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DRAGged into Conflicts: Detecting and Addressing Conflicting Sources in Search-Augmented LLMs
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DRAGged into Conflicts: Detecting and Addressing Conflicting Sources in Search-Augmented LLMs
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Retrieval Augmented Generation (RAG) is a commonly used approach for enhancing large language models (LLMs) with relevant and up-to-date information. However, the retrieved sources can often contain conflicting information and it remains unclear how models should address such discrepancies. In this work, we first propose a novel taxonomy of knowledge conflict types in RAG, along with the desired model behavior for each type. We then introduce CONFLICTS, a high-quality benchmark with expert annotations of conflict types in a realistic RAG setting. CONFLICTS is the first benchmark that enables tracking progress on how models address a wide range of knowledge conflicts. We conduct extensive experiments on this benchmark, showing that LLMs often struggle to appropriately resolve conflicts between sources. While prompting LLMs to explicitly reason about the potential conflict in the retrieved documents significantly improves the quality and appropriateness of their responses, substantial room for improvement in future research remains.
Forward citations
Cited by 9 Pith papers
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Detecting Knowledge Inconsistencies Across Text, Tables, and Knowledge Graphs
Introduces a taxonomy and an automatic pipeline (Kontrast) that compares Wikipedia table answers with Wikidata answers to detect and label cross-modal knowledge inconsistencies.
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A controlled synthetic-retrieval stress test shows that AI search agents differ greatly in handling untrustworthy, irrelevant, or false documents, and that HotpotQA is partly answerable from parametric knowledge alone.
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Empirical study finds isolation drives gains for weak models in multi-agent RAG while scoring matters for strong ones, enabling MADARA for cost-efficient adaptive assessment.
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This survey categorizes agentic environments for LLMs by eight attributes and domains, introduces symbolic and neural synthesis paradigms with evaluation, and outlines four agent evolution pathways plus three environm...
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ConflictRAG: Detecting and Resolving Knowledge Conflicts in Retrieval Augmented Generation
ConflictRAG introduces a conflict-aware RAG pipeline with two-stage detection (MLP + selective LLM), Entropy-TOPSIS credibility assessment, and a new CARS metric, reporting 88.7% F1 and 5.3-6.1% gains on benchmarks.
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ConflictRAG: Detecting and Resolving Knowledge Conflicts in Retrieval Augmented Generation
ConflictRAG adds conflict detection, source credibility assessment via Entropy-TOPSIS, and a CARS diagnostic score to RAG pipelines, reporting 88.7% F1 detection and 5.3-6.1% correctness gains on three benchmarks.
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Retrieval-Augmented Generation Must Move Beyond Factual Grounding to Represent Diverse Opinions
Opinion-aware RAG with LLM opinion extraction and entity-linked graphs improves retrieval diversity by 26-42% over factual baselines on e-commerce forum data.
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Retrieval-Augmented Generation Must Move Beyond Factual Grounding to Represent Diverse Opinions
RAG systems optimize for factual certainty and ignore opinion diversity; O-RAG reduces Wasserstein distance to corpus sentiment and is preferred by humans 79% of the time.
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