arXiv (All)
open access
Retrieval Capacity of Self-Attention Under Competition
How many tokens from its context does a language model actually use, and what determines that number? We study this question through self-attention. Without retraining, we retain only the tokens with the highest attention weights at each head, layer, and query, keeping their original weights unchang
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Language Models Can Control Their Own Attention#924873
- Language Models Can Control Their Own Attention#920639
- FrugalPrompt: Reducing Contextual Overhead in Large Language Models via Token Attribution#972975
- LLMs learn different forms of metacognition when trained to predict their own accuracy#1118959
- Fine-Tune, Then Rectify#1062812
Record · ID 1142186
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive