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From Parameters to Answers: How LLMs Retrieve and Use Their Internal Knowledge

Paper recorded by Signals 4 on 2026-09-10 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-10 on arXiv · recorded by Signals 4 on 2026-09-11

Category: cs.AI · 人工智能 · first seen 2026-09-11

Abstract

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-conditi

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#113 most recent of 300 cs.AI papers we have recorded · ↑ newer: Explainability Assistant: A Conversational XAI Interface for Interpret · ↓ older: Model-Aware Schedules Improve Generation via Fiberwise Optimal Transpo
Cite this page: From Parameters to Answers: How LLMs Retrieve and Use Their Internal Knowledge: the #113 most recent of 300 cs.AI papers we have recorded (as of 2026-09-10). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/from-parameters-to-answers-how-llms-retrieve-and-use-their-internal-knowledge.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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