研究显示大模型如何从内部知识库中检索并使用信息
From Parameters to Answers: How LLMs Retrieve and Use Their Internal Knowledge
这是篇研究大模型内部知识检索机制的论文，作者通过实验发现不同模型（如Gemma和Llama）在处理配对问题时，对全局请求方向的依赖模式存在差异。
研究通过在Qwen、Llama和Gemma模型中执行层间干预，比较了国家-大陆问题与名词、形容词和代码答案。诊断分析显示，在配对协议中，对全局请求方向的依赖从早期层到后期层逐渐减少，而对拟合内容的依赖持续存在。Qwen的配对条件方向在后期仍保持影响。
From Parameters to Answers: How LLMs Retrieve and Use Their Internal Knowledge
How does a language model's dependence on query-routing information and target knowledge change as it answers a question? We study this question through layerwise interventions on the hidden state at the end of the question. Across Qwen, Llama, and Gemma, we compare country-continent questions with noun, adjective, and code answers while keeping several fitted measurements distinct. A pair-conditioned request direction describes which country is queried in natural single-country questions; a global request direction describes first- versus second-country requests in paired questions; separate selection candidates test control among contents already available in the hidden state. A diagnostic reanalysis of frozen Qwen natural-question states shows that the pair-conditioned direction grows stronger before interventions on it begin to alter later fitted knowledge, with this causal window opening while answer-supporting content is still forming. The paired three-model trajectories are not uniform: Gemma shows a partially overlapping mid-layer routing-content profile, whereas Llama has no sustained routing-effect window under the same gates. In the paired protocol, dependence on the global request direction decreases from fixed earlier to later layer sets while dependence on fitted content persists. A matched Qwen comparison shows that the pair-conditioned direction retains a late effect, so this operational handoff concerns the global fitted direction rather than all request information. These results separate early readability, natural strength, causal steering, and later content dependence.
