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上下文或知识:大型语言模型如何为其自身答案归因

arXiv:2610.32109v1 发布类型:新 摘要:语言模型能够根据给定的文档或其参数化知识回答问题。然而,当后续被问及答案的来源时,究竟是什么决定了它们的来源报告?我们通过在答案生成之后、源问题提出之前对模型的关键值(KV)缓存进行干预来研究这一问题。在一项受控的事实性任务中,针对四款模型(7B—32B),无论改变还是移除文档的缓存,都不会影响其来源报告。相比之下,在两次运行之间移植问答(Q+A)缓存——且前后两次的问答标记完全相同——则会实现报告的转移:所有符合条件的报告都会沿用捐赠方的来源,而接收方的文档本身保持不变。在两款较小的模型上,沿着事先为任何源问题所拟合的方向引导问答值……

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arXiv:2610.32109v1 Announce Type: new

Abstract: Language models can answer a question from a supplied document or from their parametric knowledge. When later asked where the answer came from, what determines their source report? We study this by intervening on the model's key-value (KV) cache after the answer is generated and before the source question is asked. On a controlled factual task with four models (7B-32B), changing or removing the document's cache never changes the report. In contrast, transplanting the question-and-answer (Q+A) cache between runs with identical question and answer tokens transfers it: every eligible report follows the donor, while the recipient's document is unchanged. On the two smaller models, steering Q+A values along directions fitted before any source question reverses 98.9-100% of reports across four document conditions at a fixed strength, while norm-matched random directions flip at most 1.1%. The components of keys and values along fitted directions carry the transplant effect: copying them alone nearly reproduces the report change; holding them fixed nearly prevents it. Applied during answer generation, the same value directions also change which source supplies the answer, though more weakly and depending on the prompt. Source reporting in this setting thus depends causally on cached state produced while answering. We do not establish general reporting accuracy or a complete interpretation of this state.

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arXiv cs.CL
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