103 citations · 261 across the 25 of their papers we have counts for
5 papers · 1 filter
Mitigating Relational Bias on Knowledge Graphs
Yu-Neng Chuang, Kwei-Herng Lai, Ruixiang Tang +4
Knowledge graph data are prevalent in real-world applications, and knowledge graph neural networks (KGNNs) are essential techniques for knowledge graph representation learning. Alt…
Shortcut Learning of Large Language Models in Natural Language Understanding
Mengnan Du, Fengxiang He, Na Zou +2
Large language models (LLMs) have achieved state-of-the-art performance on a series of natural language understanding tasks. However, these LLMs might rely on dataset bias and arti…
Mitigating Algorithmic Bias with Limited Annotations
Guanchu Wang, Mengnan Du, Ninghao Liu +2
Existing work on fairness modeling commonly assumes that sensitive attributes for all instances are fully available, which may not be true in many real-world applications due to th…
Accelerating Shapley Explanation via Contributive Cooperator Selection
Guanchu Wang, Yu-Neng Chuang, Mengnan Du +5
Even though Shapley value provides an effective explanation for a DNN model prediction, the computation relies on the enumeration of all possible input feature coalitions, which le…
Unveiling Project-Specific Bias in Neural Code Models
Zhiming Li, Yanzhou Li, Tianlin Li +5
Deep learning has introduced significant improvements in many software analysis tasks. Although the Large Language Models (LLMs) based neural code models demonstrate commendable pe…