31 citations · 50 across the 5 of their papers we have counts for
5 papers
MegaScale-Omni: A Hyper-Scale, Workload-Resilient System for MultiModal LLM Training in Production
Chunyu Xue, Yangrui Chen, Jianyu Jiang +14
As the foundational component of versatile AI applications, training an multimodal large language model (MLLM) relies on multimodal datasets with dynamic modality mixture proportio…
Seed1.5-VL Technical Report
Dong Guo, Faming Wu, Feida Zhu +194
We present Seed1.5-VL, a vision-language foundation model designed to advance general-purpose multimodal understanding and reasoning. Seed1.5-VL is composed with a 532M-parameter v…
Exploiting Global Contextual Information for Document-level Named Entity Recognition
Zanbo Wang, Wei Wei, Xianling Mao +4
Most existing named entity recognition (NER) approaches are based on sequence labeling models, which focus on capturing the local context dependencies. However, the way of taking o…
A Survey on Recent Advances in Sequence Labeling from Deep Learning Models
Zhiyong He, Zanbo Wang, Wei Wei +3
Sequence labeling (SL) is a fundamental research problem encompassing a variety of tasks, e.g., part-of-speech (POS) tagging, named entity recognition (NER), text chunking, etc. Th…
Position-Aware Self-Attention based Neural Sequence Labeling
Wei Wei, Zanbo Wang, Xianling Mao +3
Sequence labeling is a fundamental task in natural language processing and has been widely studied. Recently, RNN-based sequence labeling models have increasingly gained attentions…