explanation evaluation

FaithLM: Towards Faithful Explanations for Large Language Models EACL 2026 HateXScore: A Metric Suite for Evaluating Reasoning Quality in Hate Speech Explanations EACL 2026 Rethinking Explanation Evaluation Under the Retraining Scheme AAAI 2026 A Causal Lens for Evaluating Faithfulness Metrics EMNLP 2025 BEE: Metric-Adapted Explanations via Baseline Exploration-Exploitation AAAI 2025 ConSim: Measuring Concept-Based Explanations’ Effectiveness with Automated Simulatability ACL 2025 FinGrAct: A Framework for FINe-GRrained Evaluation of ACTionability in Explainable Automatic Fact-Checking EMNLP 2025 Digital Socrates: Evaluating LLMs through Explanation Critiques ACL 2024 Evaluating the Effectiveness of Explainable Artificial Intelligence Approaches (Student Abstract) AAAI 2024 Inference to the Best Explanation in Large Language Models ACL 2024 Evaluating Post-hoc Explanations for Graph Neural Networks via Robustness Analysis NeurIPS 2023 Quantus: An Explainable AI Toolkit for Responsible Evaluation of Neural Network Explanations and Beyond JMLR 2023 Shortcomings of Top-Down Randomization-Based Sanity Checks for Evaluations of Deep Neural Network Explanations CVPR 2023 Explain, Edit, and Understand: Rethinking User Study Design for Evaluating Model Explanations AAAI 2022 On the Challenges of Evaluating Compositional Explanations in Multi-Hop Inference: Relevance, Completeness, and Expert Ratings EMNLP 2021 Debugging Tests for Model Explanations NeurIPS 2020 On the (In)fidelity and Sensitivity of Explanations NeurIPS 2019 Comparing Automatic and Human Evaluation of Local Explanations for Text Classification NAACL 2018