In the news
From Parameters to Answers: How LLMs Retrieve and Use Their Internal Knowledge
arXiv cs.AI · Published · 3 min read
In 30 seconds
- What happened
- Researchers studied how language models route query information and retrieve knowledge across layers when answering questions using layerwise interventions.
- Why it matters
- Engineers building interpretability tools or optimizing LLM architectures need to understand how models process and retrieve stored knowledge internally.
- Watch out
- Results vary significantly across model families; Gemma and Llama show different routing patterns, limiting generalizability of findings to all architectures.
- llm
- language model
- edge
- llama
- qwen
The patterns behind this
Each one covers how the technique works, when it earns its cost, and where it breaks.
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