Seeing through the Conflict: Transparent Knowledge Conflict Handling in Retrieval-Augmented Generation

• URL: http://arxiv.org/abs/2601.06842v1
• Date: Sun, 11 Jan 2026 10:08:49 GMT
• Title: Seeing through the Conflict: Transparent Knowledge Conflict Handling in Retrieval-Augmented Generation
• Abstract summary: TCR (Transparent Conflict Resolution) disentangles semantic match and factual consistency via dual contrastive encoders.<n>It raises knowledge-gap recovery by +21.4 pp and cuts misleading-context overrides by -29.3 pp, while adding only 0.3% parameters.<n>The signals align with human judgements and expose temporal decision patterns.
• Score: 12.469991196570106
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models (LLMs) equipped with retrieval--the Retrieval-Augmented Generation (RAG) paradigm--should combine their parametric knowledge with external evidence, yet in practice they often hallucinate, over-trust noisy snippets, or ignore vital context. We introduce TCR (Transparent Conflict Resolution), a plug-and-play framework that makes this decision process observable and controllable. TCR (i) disentangles semantic match and factual consistency via dual contrastive encoders, (ii) estimates self-answerability to gauge confidence in internal memory, and (iii) feeds the three scalar signals to the generator through a lightweight soft-prompt with SNR-based weighting. Across seven benchmarks TCR improves conflict detection (+5-18 F1), raises knowledge-gap recovery by +21.4 pp and cuts misleading-context overrides by -29.3 pp, while adding only 0.3% parameters. The signals align with human judgements and expose temporal decision patterns.

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