42 citations · 52 across the 6 of their papers we have counts for
6 papers
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…
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…
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…
BLESS: Benchmarking Large Language Models on Sentence Simplification
Tannon Kew, Alison Chi, Laura Vásquez-Rodríguez +4
We present BLESS, a comprehensive performance benchmark of the most recent state-of-the-art large language models (LLMs) on the task of text simplification (TS). We examine how wel…
Branch-Solve-Merge Improves Large Language Model Evaluation and Generation
Swarnadeep Saha, Omer Levy, Asli Celikyilmaz +3
Large Language Models (LLMs) are frequently used for multi-faceted language generation and evaluation tasks that involve satisfying intricate user constraints or taking into accoun…
Chain-of-Verification Reduces Hallucination in Large Language Models
Shehzaad Dhuliawala, Mojtaba Komeili, Jing Xu +4
Generation of plausible yet incorrect factual information, termed hallucination, is an unsolved issue in large language models. We study the ability of language models to deliberat…