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

A new research paper examines how large language models retrieve and utilize stored information by analyzing internal data processing across different model layers. By performing interventions on the hidden states of Qwen, Llama, and Gemma, researchers found distinct patterns in how models prioritize query routing versus factual recall when generating answers. These findings offer insight into the internal mechanisms of how models access their training data, potentially helping developers improve the accuracy and transparency of future systems.