Hai-Tao Zheng

Sep 10, 2023 | arxiv.org | Yangning Li |Yinghui Li |Hai-Tao Zheng |Chaiyut Luoyiching

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Jul 11, 2023 | mdpi.com | Zilin Yuan |Yinghui Li |Yangning Li |Hai-Tao Zheng

Abstract:Text classification is a well-established task in NLP, but it has two major limitations. Firstly, text classification is heavily reliant on domain-specific knowledge, meaning that a classifier that is trained on a given corpus may not perform well when presented with text from another domain. Secondly, text classification models require substantial amounts of annotated data for training, and in certain domains, there may be an insufficient quantity of labeled data available.

Apr 7, 2023 | arxiv.org | Shirong Ma |Yangning Li |Yinghui Li |Hai-Tao Zheng

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Mar 2, 2023 | nature.com | Ning Ding |Zhiyuan Liu |Hai-Tao Zheng

AbstractWith the prevalence of pre-trained language models (PLMs) and the pre-training–fine-tuning paradigm, it has been continuously shown that larger models tend to yield better performance. However, as PLMs scale up, fine-tuning and storing all the parameters is prohibitively costly and eventually becomes practically infeasible.