ArgRAG: Explainable Retrieval Augmented Generation using Quantitative Bipolar Argumentation

Retrieval-Augmented Generation (RAG) enhances large language models by incorporating external knowledge, yet suffers from critical limitations in high-stakes domains—namely, sensitivity to noisy or contradictory evidence and opaque, stochastic decision-making. We propose \textsc{ArgRAG}, an explainable, and contestable alternative that replaces black-box reasoning with structured inference using a Quantitative Bipolar Argumentation Framework (QBAF). \textsc{ArgRAG} constructs a QBAF from retrieved documents and performs deterministic reasoning under gradual semantics. This allows faithfully explanaining and contesting decisions. Evaluated on two fact verification benchmarks, PubHealth and RAGuard, \textsc{ArgRAG} achieves strong accuracy while significantly improving transparency.

Authors

• Yuqicheng Zhu
• Nico Potyka
• Daniel Hernández 0002
• Yuan He 0008
• Zifeng Ding
• Bo Xiong 0001
• Dongzhuoran Zhou
• Evgeny Kharlamov
• Steffen Staab

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