no code implementations • 14 Mar 2024 • Chang Zong, Yuyan Chen, Weiming Lu, Jian Shao, Yueting Zhuang
Large Language Models (LLMs) have demonstrated efficacy in various linguistic applications, including text summarization and controlled text generation.
no code implementations • 22 Feb 2024 • Chang Zong, Yuchen Yan, Weiming Lu, Jian Shao, Eliot Huang, Heng Chang, Yueting Zhuang
We evaluated the performance of our framework using three benchmark datasets, and the results show that our framework outperforms state-of-the-art systems on the LC-QuAD and YAGO-QA benchmarks, yielding F1 scores of 11. 8% and 20. 7%, respectively.
no code implementations • 2 Oct 2022 • Chang Zong, Yueting Zhuang, Weiming Lu, Jian Shao, Siliang Tang
In this paper, we propose CTPIR, a new citation trajectory prediction framework that is able to represent the influence (the momentum of citation) of either new or existing publications using the history information of all their attributes.
no code implementations • 1 Jan 2021 • Dong Chen, Lingfei Wu, Siliang Tang, Fangli Xu, Juncheng Li, Chang Zong, Chilie Tan, Yueting Zhuang
In particular, we first cast the meta-overfitting problem (overfitting on sampling and label noise) as a gradient noise problem since few available samples cause meta-learner to overfit on existing examples (clean or corrupted) of an individual task at every gradient step.
no code implementations • 1 Jan 2021 • Chengyue Huang, Lingfei Wu, Yadong Ding, Siliang Tang, Fangli Xu, Chang Zong, Chilie Tan, Yueting Zhuang
To this end, we learn a differentiable graph neural network as a surrogate model to rank candidate architectures, which enable us to obtain gradient w. r. t the input architectures.