110 citations · 375 across the 10 of their papers we have counts for
10 papers
Qwen Technical Report
Jinze Bai, Shuai Bai, Yunfei Chu +45
Large language models (LLMs) have revolutionized the field of artificial intelligence, enabling natural language processing tasks that were previously thought to be exclusive to hu…
Grid-VLP: Revisiting Grid Features for Vision-Language Pre-training
Ming Yan, Haiyang Xu, Chenliang Li +4
Existing approaches to vision-language pre-training (VLP) heavily rely on an object detector based on bounding boxes (regions), where salient objects are first detected from images…
StructuralLM: Structural Pre-training for Form Understanding
Chenliang Li, Bin Bi, Ming Yan +4
Large pre-trained language models achieve state-of-the-art results when fine-tuned on downstream NLP tasks. However, they almost exclusively focus on text-only representation, whil…
SemVLP: Vision-Language Pre-training by Aligning Semantics at Multiple Levels
Chenliang Li, Ming Yan, Haiyang Xu +4
Vision-language pre-training (VLP) on large-scale image-text pairs has recently witnessed rapid progress for learning cross-modal representations. Existing pre-training methods eit…
VECO: Variable and Flexible Cross-lingual Pre-training for Language Understanding and Generation
Fuli Luo, Wei Wang, Jiahao Liu +5
Existing work in multilingual pretraining has demonstrated the potential of cross-lingual transferability by training a unified Transformer encoder for multiple languages. However,…
PALM: Pre-training an Autoencoding&Autoregressive Language Model for Context-conditioned Generation
Bin Bi, Chenliang Li, Chen Wu +5
Self-supervised pre-training, such as BERT, MASS and BART, has emerged as a powerful technique for natural language understanding and generation. Existing pre-training techniques e…