model transparency

A Knowledge Distillation-Based Approach to Enhance Transparency of Classifier Models AAAI 2025 Connecting Concept Layers and Rationales to Enhance Language Model Interpretability EMNLP 2025 Evaluating Design Choices in Verifiable Generation with Open-source Models NAACL 2025 Has this Fact been Edited? Detecting Knowledge Edits in Language Models NAACL 2025 Low-Perplexity LLM-Generated Sequences and Where To Find Them ACL 2025 o-MEGA: Optimized Methods for Explanation Generation and Analysis EMNLP 2025 Connecting the Dots in News Analysis: Bridging the Cross-Disciplinary Disparities in Media Bias and Framing NAACL 2024 TokenSHAP: Interpreting Large Language Models with Monte Carlo Shapley Value Estimation EMNLP 2024 Data Portraits: Recording Foundation Model Training Data NeurIPS 2023 How to address monotonicity for model risk management? ICML 2023 Bayes-TrEx: a Bayesian Sampling Approach to Model Transparency by Example AAAI 2021 dalex: Responsible Machine Learning with Interactive Explainability and Fairness in Python JMLR 2021 Hybrid Predictive Models: When an Interpretable Model Collaborates with a Black-box Model JMLR 2021 What Would it Take to get Biomedical QA Systems into Practice? EMNLP 2021 Learning to Faithfully Rationalize by Construction ACL 2020 Transparent Classification with Multilayer Logical Perceptrons and Random Binarization AAAI 2020 Auditing Deep Learning processes through Kernel-based Explanatory Models EMNLP 2019 Neural Interaction Transparency (NIT): Disentangling Learned Interactions for Improved Interpretability NeurIPS 2018 Transparency by Design: Closing the Gap Between Performance and Interpretability in Visual Reasoning CVPR 2018