Large language models answer knowledge-intensive questions using both parametric memory and retrieved evidence, but neither source is uniformly reliable. Retrieval can fill knowledge gaps, yet distracting passages may override correct closed-book answers. We study this post-generation conflict as answer-level source arbitration: given Direct and RAG answers from the same frozen model, decide which source to trust. We propose TRUSTMARGIN, a training-free, plug-and-play arbitration layer that scores the two existing candidates with the model's own likelihoods. It combines a parametric-prior margin, which tests whether memory accepts the retrieved answer, with an evidence-binding margin, which discounts passage-only salience and measures question-specific support. TRUSTMARGIN selects between Direct and RAG without fine-tuning, external judges, or additional generation. Across 2WIKIMQA and CWQA with three LLaMA scales, TRUSTMARGIN consistently improves over Direct generation and BM25-RAG, recovers part of the Direct/RAG oracle gap, and generalizes to multiple training-free RAG pipelines.

翻译：大语言模型通过参数化记忆与检索证据共同回答知识密集型问题，但两者均非完全可靠。检索虽能填补知识空白，但干扰性段落可能覆盖原本正确的闭卷答案。我们将这种生成后冲突定位为答案级来源仲裁问题：给定同一冻结模型生成的直接回答与检索增强回答，需判定应信任哪个来源。我们提出免训练即插即用仲裁层TRUSTMARGIN，通过模型自身似然度对两个候选答案进行评分。该机制结合两项关键指标：参数化先验边际（检验记忆是否接受检索答案）与证据绑定边际（消解段落级显著性并量化问题特异性支持度）。无需微调、外部评判或额外生成，TRUSTMARGIN即可在直接生成与检索增强生成之间做出选择。在2WIKIMQA与CWQA两个基准测试中，基于三种不同规模的LLaMA模型，TRUSTMARGIN均持续优于直接生成与BM25检索增强生成，部分弥合了直接生成/检索增强生成理想性能差距，并可泛化至多种免训练检索增强生成流水线。