3 papers
cs.CL2025
TiME: Tiny Monolingual Encoders for Efficient NLP Pipelines
David Schulmeister, Valentin Hartmann, Lars Klein +1
Today, a lot of research on language models is focused on large, general-purpose models. However, many NLP pipelines only require models with a well-defined, small set of capabilit…
cs.CL2025
zip2zip: Inference-Time Adaptive Tokenization via Online Compression
Saibo Geng, Nathan Ranchin, Yunzhen yao +4
Tokenization efficiency plays a critical role in the performance and cost of large language models (LLMs), yet most models rely on static tokenizers optimized on general-purpose co…
cs.CL2025
JSONSchemaBench: A Rigorous Benchmark of Structured Outputs for Language Models
Saibo Geng, Hudson Cooper, MichaÅ Moskal +6
Reliably generating structured outputs has become a critical capability for modern language model (LM) applications. Constrained decoding has emerged as the dominant technology acr…