arXiv (All)
open access
Counting and Min-Cost Encoding for Tokenization in Large Language Models
Mainstream large language models rely on a tokenizer to encode text into a token sequence. Different tokenizers may yield token sequences of substantially different lengths for the same text. With a fixed model architecture, shorter token sequences correspond to lower inference time. We propose a to
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
- Resolution as a First-Class Decision: Task-Conditioned Routing for Efficient Multimodal Large Language Models#1129550
- Advantageous Parameter Expansion Training Makes Better Large Language Models#670342
- Advantageous Parameter Expansion Training Makes Better Large Language Models#668277
- Optimizing Large Language Models: Metrics, Energy Efficiency, and Case Study Insights#139146
- Robust Text Watermarking for Large Language Models via Dual Semantic Embeddings#927363
Record · ID 1156062
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