most citedFairness-guided Few-shot Prompting for Large Language Models

24 citations · 38 across the 8 of their papers we have counts for

collaborators

8 papers

cs.CL2023

Rethinking Word-Level Auto-Completion in Computer-Aided Translation

Xingyu Chen, Lemao Liu, Guoping Huang +4

Word-Level Auto-Completion (WLAC) plays a crucial role in Computer-Assisted Translation. It aims at providing word-level auto-completion suggestions for human translators. While pr…

cs.CL2023

IMTLab: An Open-Source Platform for Building, Evaluating, and Diagnosing Interactive Machine Translation Systems

Xu Huang, Zhirui Zhang, Ruize Gao +6

We present IMTLab, an open-source end-to-end interactive machine translation (IMT) system platform that enables researchers to quickly build IMT systems with state-of-the-art model…

cs.CL2023

Rethinking Translation Memory Augmented Neural Machine Translation

Hongkun Hao, Guoping Huang, Lemao Liu +3

This paper rethinks translation memory augmented neural machine translation (TM-augmented NMT) from two perspectives, i.e., a probabilistic view of retrieval and the variance-bias…

cs.CL2023

E-NER: Evidential Deep Learning for Trustworthy Named Entity Recognition

Zhen Zhang, Mengting Hu, Shiwan Zhao +6

Most named entity recognition (NER) systems focus on improving model performance, ignoring the need to quantify model uncertainty, which is critical to the reliability of NER syste…

cs.CL202324 cited

Fairness-guided Few-shot Prompting for Large Language Models

Huan Ma, Changqing Zhang, Yatao Bian +7

Large language models have demonstrated surprising ability to perform in-context learning, i.e., these models can be directly applied to solve numerous downstream tasks by conditio…

cs.CL20233 cited

Federated Nearest Neighbor Machine Translation

Yichao Du, Zhirui Zhang, Bingzhe Wu +3

To protect user privacy and meet legal regulations, federated learning (FL) is attracting significant attention. Training neural machine translation (NMT) models with traditional F…