3 papers
cs.CL2026
ByteFlow: Language Modeling through Adaptive Byte Compression without a Tokenizer
Chunyuan Deng, Sanket Lokegaonkar, Colin Lockard +3
Modern language models still rely on fixed, pre-defined subword tokenizations. Once a tokenizer is trained, the LM can only operate at this fixed level of granularity, which often…
cs.CV2025
Train a Unified Multimodal Data Quality Classifier with Synthetic Data
Weizhi Wang, Rongmei Lin, Shiyang Li +7
The Multimodal Large Language Models (MLLMs) are continually pre-trained on a mixture of image-text caption data and interleaved document data, while the high-quality data filterin…
cs.CL2024
Multi-head Sequence Tagging Model for Grammatical Error Correction
Kamal Al-Sabahi, Kang Yang, Wangwang Liu +3
To solve the Grammatical Error Correction (GEC) problem , a mapping between a source sequence and a target one is needed, where the two differ only on few spans. For this reason, t…