Seeing through the Conflict: Transparent Knowledge Conflict Handling in Retrieval-Augmented Generation
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866918281529524224 |
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| author | Ye, Hua Chen, Siyuan Zhong, Ziqi Xiao, Canran Zhang, Haoliang Wu, Yuhan Shen, Fei |
| author_facet | Ye, Hua Chen, Siyuan Zhong, Ziqi Xiao, Canran Zhang, Haoliang Wu, Yuhan Shen, Fei |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_06842 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Seeing through the Conflict: Transparent Knowledge Conflict Handling in Retrieval-Augmented Generation Ye, Hua Chen, Siyuan Zhong, Ziqi Xiao, Canran Zhang, Haoliang Wu, Yuhan Shen, Fei Artificial Intelligence I.2.7; H.3.3 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. |
| title | Seeing through the Conflict: Transparent Knowledge Conflict Handling in Retrieval-Augmented Generation |
| topic | Artificial Intelligence I.2.7; H.3.3 |
| url | https://arxiv.org/abs/2601.06842 |