arXiv cs.AI· Minh Pham Van, Cong Tran·· 11 小时前AI 评分34
知识冲突不该靠固定规则：GRACE 基准与先验驱动的双源解码方法
Don't Pick a Winner: Knowledge Conflicts Need a Prior, Not a Rule
AI 导读
针对检索增强模型在上下文与自身知识冲突时依赖固定规则的问题，研究者提出需分别保留两个来源的证据并引入“上下文正确频率”的先验，在 GRACE 基准上以真实正确比例设定先验 π 后，各上下文混合比例下均优于最佳基线，最高领先 26 分。
正文 · 原文
Abstract:A retrieval-augmented language model answers from two sources, the retrieved context and its own knowledge, and the two disagree whenever the context is incomplete or wrong. Existing methods settle the disagreement with a rule fixed at design time: follow the context, amplify it in proportion to how far it moves the model, or trust the model's memory. Each rule is confidently wrong in some deployment. In a decision-theoretic model of two sources, the score of any fixed rule is linear in the share of reliable contexts while the best attainable score is convex, so every fixed rule is beaten wherever it is not exactly optimal; and a symmetric divergence between the two sources, the signal adaptive decoding uses, carries no information about which one is right. Resolving a conflict therefore needs two inputs that no existing method keeps: evidence about each source separately, and a prior on how often the context is right. We test these claims on GRACE, a benchmark that intervenes on the knowledge-graph context of a fixed question, so that what the model knows, what the context says and what is true are measured separately. There, every baseline from four families is strong on right contexts or on wrong ones, never on both. We then build the two inputs: the model states its knowledge as notes, a small policy voices each note with a calibrated confidence, a reader answers in two voices, and a selector commits or abstains under a prior $\pi$ applied as an exact prior shift. With $\pi$ set to the true share of right contexts, the system is above the best baseline at every mixture of GRACE contexts, by up to 26 points. Its overall score is 45.3 and 35.0 (Qwen3.5-35B-A3B, Llama-3.1-8B), against 29.4 and 27.5 for the best baseline. On preregistered ClashEval it beats same-backbone CK-PLUG and TCR by 12 and 26 points. Where the model lacks the knowledge, the gains vanish, as the theory predicts.
| Comments: | 49 pages (9 main text + appendix and supplementary material), 14 figures |
| Subjects: | Artificial Intelligence (cs.AI); Computation and Language (cs.CL) |
| Cite as: | arXiv:2610.36169 [cs.AI] |
| (or arXiv:2610.36169v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.36169 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Minh Pham Van [view email]
[v1]
Mon, 26 Oct 2026 19:03:29 UTC (440 KB)
来源：arXiv cs.AI · arxiv.org