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cs.LG2026
Interpretable and Fair Generalized Additive Neural Networks via Multi-objective Learning
Ziming Wang, Changwu Huang, Ke Tang +2
Interpretability and fairness are two of the most emphasized dimensions in trustworthy artificial intelligence (AI). Various explainable AI methods have been introduced to improve…
cs.CL2026
CASE: Causal Alignment and Structural Enforcement for Improving Chain-of-Thought Faithfulness
Ziming Wang, Yinghua Yao, Changwu Huang +2
Chain-of-thought (CoT) reasoning is widely used to improve both the performance and interpretability of large language models (LLMs), yet the generated reasoning may not faithfully…
cs.LG2026
Integrating Knowledge Distillation Methods: A Sequential Multi-Stage Framework
Yinxi Tian, Changwu Huang, Ke Tang +1
Knowledge distillation (KD) transfers knowledge from large teacher models to compact student models, enabling efficient deployment on resource constrained devices. While diverse KD…