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

TL;DR

This paper presents TCR, a framework that enhances transparency and control in retrieval-augmented generation by making conflict resolution observable, improving factual accuracy, and reducing hallucinations in large language models.

Contribution

TCR introduces a novel, plug-and-play method that disentangles semantic and factual signals, estimates answerability, and improves conflict handling in RAG models.

Findings

Improves conflict detection F1 by 5-18 points

Increases knowledge-gap recovery by 21.4 percentage points

Reduces misleading-context overrides by 29.3 percentage points

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.