99 citations · 147 across the 4 of their papers we have counts for
4 papers
Bridge the Gap between Language models and Tabular Understanding
Nuo Chen, Linjun Shou, Ming Gong +5
Table pretrain-then-finetune paradigm has been proposed and employed at a rapid pace after the success of pre-training in the natural language domain. Despite the promising finding…
Fine-Tuning Pre-Trained Language Models Effectively by Optimizing Subnetworks Adaptively
Haojie Zhang, Ge Li, Jia Li +3
Large-scale pre-trained language models have achieved impressive results on a wide range of downstream tasks recently. However, fine-tuning an extremely large-scale pre-trained lan…
CodeEditor: Learning to Edit Source Code with Pre-trained Models
Jia Li, Ge Li, Zhuo Li +4
Developers often perform repetitive code editing activities for various reasons (e.g., code refactoring) during software development. Pre-trained code editing models have achieved…
TUTA: Tree-based Transformers for Generally Structured Table Pre-training
Zhiruo Wang, Haoyu Dong, Ran Jia +4
Tables are widely used with various structures to organize and present data. Recent attempts on table understanding mainly focus on relational tables, yet overlook to other common…