4 citations · 11 across the 7 of their papers we have counts for
7 papers
VANER: Leveraging Large Language Model for Versatile and Adaptive Biomedical Named Entity Recognition
Junyi Biana, Weiqi Zhai, Xiaodi Huang +2
Prevalent solution for BioNER involves using representation learning techniques coupled with sequence labeling. However, such methods are inherently task-specific, demonstrate poor…
HeLM: Highlighted Evidence augmented Language Model for Enhanced Table-to-Text Generation
Junyi Bian, Xiaolei Qin, Wuhe Zou +4
Large models have demonstrated significant progress across various domains, particularly in tasks related to text generation. In the domain of Table to Text, many Large Language Mo…
Inspire the Large Language Model by External Knowledge on BioMedical Named Entity Recognition
Junyi Bian, Jiaxuan Zheng, Yuyi Zhang +1
Large language models (LLMs) have demonstrated dominating performance in many NLP tasks, especially on generative tasks. However, they often fall short in some information extracti…
DMNER: Biomedical Entity Recognition by Detection and Matching
Junyi Bian, Rongze Jiang, Weiqi Zhai +3
Biomedical named entity recognition (BNER) serves as the foundation for numerous biomedical text mining tasks. Unlike general NER, BNER require a comprehensive grasp of the domain,…
GoSum: Extractive Summarization of Long Documents by Reinforcement Learning and Graph Organized discourse state
Junyi Bian, Xiaodi Huang, Hong Zhou +1
Extracting summaries from long documents can be regarded as sentence classification using the structural information of the documents. How to use such structural information to sum…
Controllable Length Control Neural Encoder-Decoder via Reinforcement Learning
Junyi Bian, Baojun Lin, Ke Zhang +3
Controlling output length in neural language generation is valuable in many scenarios, especially for the tasks that have length constraints. A model with stronger length control c…
Hybrid Function Sparse Representation towards Image Super Resolution
Junyi Bian, Baojun Lin, Ke Zhang
Sparse representation with training-based dictionary has been shown successful on super resolution(SR) but still have some limitations. Based on the idea of making the magnificatio…