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cs.LG2025
FaCT: Faithful Concept Traces for Explaining Neural Network Decisions
Amin Parchami-Araghi, Sukrut Rao, Jonas Fischer +1
Deep networks have shown remarkable performance across a wide range of tasks, yet getting a global concept-level understanding of how they function remains a key challenge. Many po…
cs.CL2025
B-cos LM: Efficiently Transforming Pre-trained Language Models for Improved Explainability
Yifan Wang, Sukrut Rao, Ji-Ung Lee +2
Post-hoc explanation methods for black-box models often struggle with faithfulness and human interpretability due to the lack of explainability in current neural architectures. Mea…