225 citations · 270 across the 11 of their papers we have counts for
14 papers · 1 filter
VibeVoice Technical Report
Zhiliang Peng, Jianwei Yu, Wenhui Wang +10
This report presents VibeVoice, a novel model designed to synthesize long-form speech with multiple speakers by employing next-token diffusion, which is a unified method for modeli…
Multimodal Latent Language Modeling with Next-Token Diffusion
Yutao Sun, Hangbo Bao, Wenhui Wang +5
Multimodal generative models require a unified approach to handle both discrete data (e.g., text and code) and continuous data (e.g., image, audio, video). In this work, we propose…
RedStone: Curating General, Code, Math, and QA Data for Large Language Models
Yaoyao Chang, Lei Cui, Li Dong +13
Pre-training Large Language Models (LLMs) on high-quality, meticulously curated datasets is widely recognized as critical for enhancing their performance and generalization capabil…
AutoDistil: Few-shot Task-agnostic Neural Architecture Search for Distilling Large Language Models
Dongkuan Xu, Subhabrata Mukherjee, Xiaodong Liu +5
Knowledge distillation (KD) methods compress large models into smaller students with manually-designed student architectures given pre-specified computational cost. This requires s…
s2s-ft: Fine-Tuning Pretrained Transformer Encoders for Sequence-to-Sequence Learning
Hangbo Bao, Li Dong, Wenhui Wang +2
Pretrained bidirectional Transformers, such as BERT, have achieved significant improvements in a wide variety of language understanding tasks, while it is not straightforward to di…
Adapt-and-Distill: Developing Small, Fast and Effective Pretrained Language Models for Domains
Yunzhi Yao, Shaohan Huang, Wenhui Wang +2
Large pre-trained models have achieved great success in many natural language processing tasks. However, when they are applied in specific domains, these models suffer from domain…