interpretability method

Diffusion-CAM: Faithful Visual Explanations for dMLLMs ACL 2026 Back Attention: Understanding and Enhancing Multi-Hop Reasoning in Large Language Models EMNLP 2025 Contrast-CAT: Contrasting Activations for Enhanced Interpretability in Transformer-based Text Classifiers UAI 2025 Do Transformer Interpretability Methods Transfer to RNNs? AAAI 2025 Fact Recall, Heuristics or Pure Guesswork? Precise Interpretations of Language Models for Fact Completion ACL 2025 Mechanistic Understanding and Mitigation of Language Model Non-Factual Hallucinations EMNLP 2024 Opening the Black Box: Unraveling the Classroom Dialogue Analysis (Student Abstract) AAAI 2024 RAVEL: Evaluating Interpretability Methods on Disentangling Language Model Representations ACL 2024 A Tale of Pronouns: Interpretability Informs Gender Bias Mitigation for Fairer Instruction-Tuned Machine Translation EMNLP 2023 Counterfactuals of Counterfactuals: a back-translation-inspired approach to analyse counterfactual editors ACL 2023 Dynamic Top-k Estimation Consolidates Disagreement between Feature Attribution Methods EMNLP 2023 Evaluating the Robustness of Interpretability Methods through Explanation Invariance and Equivariance NeurIPS 2023 Explainability as statistical inference ICML 2023 Overlooked Factors in Concept-Based Explanations: Dataset Choice, Concept Learnability, and Human Capability CVPR 2023 PDiscoNet: Semantically consistent part discovery for fine-grained recognition ICCV 2023 Which Explanation Makes Sense? A Critical Evaluation of Local Explanations for Assessing Cervical Cancer Risk MLHC 2023 Are Multilingual Sentiment Models Equally Right for the Right Reasons? EMNLP 2022 Identifying and Mitigating Spurious Correlations for Improving Robustness in NLP Models NAACL 2022 What does LIME really see in images? ICML 2021 A Benchmark for Interpretability Methods in Deep Neural Networks NeurIPS 2019 Attacks Meet Interpretability: Attribute-steered Detection of Adversarial Samples NeurIPS 2018