Publications (38)
Boosting Explainability through Selective Rationalization in Pre-trained Language Models
Libing Yuan, Shuaibo Hu, Kui Yu +1
The widespread application of pre-trained language models (PLMs) in natural language processing (NLP) has led to increasing concerns about their explainability. Selective rationali…
Redefining Simplicity: Benchmarking Large Language Models from Lexical to Document Simplification
Jipeng Qiang, Minjiang Huang, Yi Zhu +3
Text simplification (TS) refers to the process of reducing the complexity of a text while retaining its original meaning and key information. Existing work only shows that large la…
A Local Similarity-Preserving Framework for Nonlinear Dimensionality Reduction with Neural Networks
Xiang Wang, Xiaoyong Li, Junxing Zhu +5
Real-world data usually have high dimensionality and it is important to mitigate the curse of dimensionality. High-dimensional data are usually in a coherent structure and make the…
Disentangled Double Machine Learning for Accurate Causal Effect Estimation
Guodu Xiang, Kui Yu, Yujie Wang +3
Confounding bias is a key challenge in causal effect estimation from observational data. Double Machine Learning (DML) addresses this issue by estimating treatment and outcome nuis…
Linking Model Intervention to Causal Interpretation in Model Explanation
Debo Cheng, Ziqi Xu, Jiuyong Li +4
Intervention intuition is often used in model explanation where the intervention effect of a feature on the outcome is quantified by the difference of a model prediction when the f…
Learning causal representations for robust domain adaptation
Shuai Yang, Kui Yu, Fuyuan Cao +3
Domain adaptation solves the learning problem in a target domain by leveraging the knowledge in a relevant source domain. While remarkable advances have been made, almost all exist…