Zhaocong Li

According to our database, Zhaocong Li authored at least 8 papers between 2022 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Can LLMs Learn Uncertainty on Their Own? Expressing Uncertainty Effectively in A Self-Training Manner.

Shudong Liu, Zhaocong Li, Xuebo Liu, Runzhe Zhan, Derek F. Wong, Lidia S. Chao, Min Zhang

Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 2024

Modular Anti-noise Deep Learning Network for Robotic Grasp Detection Based on RGB Images.

Zhaocong Li, Jiahao Zhong

Proceedings of the 7th International Conference on Electronics and Electrical Engineering Technology, 2024

Towards Demonstration-Aware Large Language Models for Machine Translation.

Chen Li, Meishan Zhang, Xuebo Liu, Zhaocong Li, Derek F. Wong, Min Zhang

Proceedings of the Findings of the Association for Computational Linguistics, 2024

2023

Modular Anti-noise Deep Learning Network for Robotic Grasp Detection Based on RGB Images.

Zhaocong Li

CoRR, 2023

TempoSum: Evaluating the Temporal Generalization of Abstractive Summarization.

Chi Seng Cheang, Hou Pong Chan, Derek F. Wong, Xuebo Liu, Zhaocong Li, Yanming Sun, Shudong Liu, Lidia S. Chao

CoRR, 2023

Can LMs Generalize to Future Data? An Empirical Analysis on Text Summarization.

Chi Seng Cheang, Hou Pong Chan, Derek F. Wong, Xuebo Liu, Zhaocong Li, Yanming Sun, Shudong Liu, Lidia S. Chao

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 2023

kNN-TL: k-Nearest-Neighbor Transfer Learning for Low-Resource Neural Machine Translation.

Shudong Liu, Xuebo Liu, Derek F. Wong, Zhaocong Li, Wenxiang Jiao, Lidia S. Chao, Min Zhang

Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2023

2022

ConsistTL: Modeling Consistency in Transfer Learning for Low-Resource Neural Machine Translation.

Zhaocong Li, Xuebo Liu, Derek F. Wong, Lidia S. Chao, Min Zhang

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, 2022