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20232025
most citedCo-clustering for Federated Recommender System

27 citations · 59 across the 9 of their papers we have counts for

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Showing 2024Show all

5 papers · 1 filter

cs.IR2024★ 27 cited

Co-clustering for Federated Recommender System

Xinrui He, Shuo Liu, Jackey Keung +1

As data privacy and security attract increasing attention, Federated Recommender System (FRS) offers a solution that strikes a balance between providing high-quality recommendation…

cs.CL2024★ 26 cited

Bias in Large Language Models: Origin, Evaluation, and Mitigation

Yufei Guo, Muzhe Guo, Juntao Su +5

Large Language Models (LLMs) have revolutionized natural language processing, but their susceptibility to biases poses significant challenges. This comprehensive review examines th…

cs.SE2024

Exploring and Lifting the Robustness of LLM-powered Automated Program Repair with Metamorphic Testing

Pengyu Xue, Linhao Wu, Zhen Yang +8

In recent years, Large language model-powered Automated Program Repair (LAPR) techniques have achieved state-of-the-art bug-fixing performance and have been pervasively applied and…

cs.SE2024★ 3 cited

Exploring and Unleashing the Power of Large Language Models in Automated Code Translation

Zhen Yang, Fang Liu, Zhongxing Yu +7

Code translation tools (transpilers) are developed for automatic source-to-source translation. Although learning-based transpilers have shown impressive enhancement against rule-ba…

cs.SE2024★ 2 cited

Delving into Parameter-Efficient Fine-Tuning in Code Change Learning: An Empirical Study

Shuo Liu, Jacky Keung, Zhen Yang +3

Compared to Full-Model Fine-Tuning (FMFT), Parameter Efficient Fine-Tuning (PEFT) has demonstrated superior performance and lower computational overhead in several code understandi…