{"total":96,"rows":[{"id":"2108.11896","title":"A Survey on Automated Fact-Checking","created":"2021-08-26","cited_by_count":378,"area":null},{"id":"2211.08332","title":"Versatile Diffusion: Text, Images and Variations All in One Diffusion Model","created":"2022-11-15","cited_by_count":199,"area":"diffusion"},{"id":"2102.12060","title":"Teach Me to Explain: A Review of Datasets for Explainable Natural Language Processing","created":"2021-02-24","cited_by_count":126,"area":null},{"id":"2305.18029","title":"Faithfulness Tests for Natural Language Explanations","created":"2023-05-29","cited_by_count":107,"area":null},{"id":"2104.14403","title":"Do Feature Attribution Methods Correctly Attribute Features?","created":"2021-04-27","cited_by_count":98,"area":null},{"id":"2111.08284","title":"Few-Shot Self-Rationalization with Natural Language Prompts","created":"2021-11-16","cited_by_count":83,"area":null},{"id":"2103.13084","title":"Paragraph-level Rationale Extraction through Regularization: A case study on European Court of Human Rights Cases","created":"2021-03-24","cited_by_count":77,"area":null},{"id":"2104.07644","title":"ExplaGraphs: An Explanation Graph Generation Task for Structured Commonsense Reasoning","created":"2021-04-15","cited_by_count":58,"area":null},{"id":"2206.07869","title":"Let Invariant Rationale Discovery Inspire Graph Contrastive Learning","created":"2022-06-16","cited_by_count":57,"area":null},{"id":"2103.12279","title":"SelfExplain: A Self-Explaining Architecture for Neural Text Classifiers","created":"2021-03-23","cited_by_count":44,"area":null},{"id":"2403.20009","title":"On Large Language Models' Hallucination with Regard to Known Facts","created":"2024-03-29","cited_by_count":43,"area":null},{"id":"2210.06929","title":"On the Explainability of Natural Language Processing Deep Models","created":"2022-10-13","cited_by_count":38,"area":null},{"id":"2110.13880","title":"Understanding Interlocking Dynamics of Cooperative Rationalization","created":"2021-10-26","cited_by_count":36,"area":null},{"id":"2204.11790","title":"Can Rationalization Improve Robustness?","created":"2022-04-25","cited_by_count":36,"area":null},{"id":"2108.12099","title":"Learning to Give Checkable Answers with Prover-Verifier Games","created":"2021-08-27","cited_by_count":32,"area":null},{"id":"2112.08802","title":"UNIREX: A Unified Learning Framework for Language Model Rationale Extraction","created":"2021-12-16","cited_by_count":32,"area":null},{"id":"2103.11072","title":"Local Interpretations for Explainable Natural Language Processing: A Survey","created":"2021-03-20","cited_by_count":31,"area":null},{"id":"2306.02873","title":"DecompX: Explaining Transformers Decisions by Propagating Token Decomposition","created":"2023-06-05","cited_by_count":31,"area":null},{"id":"2110.04392","title":"The Eval4NLP Shared Task on Explainable Quality Estimation: Overview and Results","created":"2021-10-08","cited_by_count":27,"area":null},{"id":"2112.00071","title":"What to Learn, and How: Toward Effective Learning from Rationales","created":"2021-11-30","cited_by_count":25,"area":null},{"id":"2305.17075","title":"CREST: A Joint Framework for Rationalization and Counterfactual Text Generation","created":"2023-05-26","cited_by_count":24,"area":null},{"id":"2108.12197","title":"Translation Error Detection as Rationale Extraction","created":"2021-08-27","cited_by_count":22,"area":null},{"id":"2301.08912","title":"Rationalization for Explainable NLP: A Survey","created":"2023-01-21","cited_by_count":22,"area":null},{"id":"2309.13391","title":"D-Separation for Causal Self-Explanation","created":"2023-09-23","cited_by_count":21,"area":null},{"id":"2105.04837","title":"Rationalization through Concepts","created":"2021-05-11","cited_by_count":19,"area":null},{"id":"2109.04552","title":"SPECTRA: Sparse Structured Text Rationalization","created":"2021-09-09","cited_by_count":19,"area":null},{"id":"2110.10470","title":"Interpreting Deep Learning Models in Natural Language Processing: A Review","created":"2021-10-20","cited_by_count":19,"area":null},{"id":"2306.14115","title":"Towards Trustworthy Explanation: On Causal Rationalization","created":"2023-06-25","cited_by_count":19,"area":null},{"id":"2209.08285","title":"FR: Folded Rationalization with a Unified Encoder","created":"2022-09-17","cited_by_count":18,"area":null},{"id":"2110.07550","title":"The Irrationality of Neural Rationale Models","created":"2021-10-14","cited_by_count":16,"area":null},{"id":"2305.06754","title":"COCKATIEL: COntinuous Concept ranKed ATtribution with Interpretable ELements for explaining neural net classifiers on NLP tasks","created":"2023-05-11","cited_by_count":16,"area":null},{"id":"2106.01354","title":"multiPRover: Generating Multiple Proofs for Improved Interpretability in Rule Reasoning","created":"2021-06-02","cited_by_count":15,"area":null},{"id":"2109.01962","title":"Counterfactual Evaluation for Explainable AI","created":"2021-09-05","cited_by_count":15,"area":null},{"id":"2201.11239","title":"Diagnosing AI Explanation Methods with Folk Concepts of Behavior","created":"2022-01-27","cited_by_count":15,"area":null},{"id":"2204.08892","title":"A survey on improving NLP models with human explanations","created":"2022-04-19","cited_by_count":15,"area":null},{"id":"2205.11097","title":"A Fine-grained Interpretability Evaluation Benchmark for Neural NLP","created":"2022-05-23","cited_by_count":15,"area":null},{"id":"2312.04103","title":"Enhancing the Rationale-Input Alignment for Self-explaining Rationalization","created":"2023-12-07","cited_by_count":15,"area":null},{"id":"2106.00320","title":"Distribution Matching for Rationalization","created":"2021-06-01","cited_by_count":14,"area":null},{"id":"2305.12962","title":"Distilling ChatGPT for Explainable Automated Student Answer Assessment","created":"2023-05-22","cited_by_count":14,"area":"distill"},{"id":"2305.13599","title":"Decoupled Rationalization with Asymmetric Learning Rates: A Flexible Lipschitz Restraint","created":"2023-05-23","cited_by_count":14,"area":null},{"id":"2109.08259","title":"Self-training with Few-shot Rationalization: Teacher Explanations Aid Student in Few-shot NLU","created":"2021-09-17","cited_by_count":13,"area":"synth"},{"id":"2303.15714","title":"Explicit Planning Helps Language Models in Logical Reasoning","created":"2023-03-28","cited_by_count":12,"area":"lr"},{"id":"2305.04492","title":"MGR: Multi-generator Based Rationalization","created":"2023-05-08","cited_by_count":12,"area":null},{"id":"2402.13098","title":"ELAD: Explanation-Guided Large Language Models Active Distillation","created":"2024-02-20","cited_by_count":12,"area":null},{"id":"2212.13261","title":"Explainable AI for Bioinformatics: Methods, Tools, and Applications","created":"2022-12-25","cited_by_count":11,"area":null},{"id":"2410.06003","title":"Is the MMI Criterion Necessary for Interpretability? Degenerating Non-causal Features to Plain Noise for Self-Rationalization","created":"2024-10-08","cited_by_count":11,"area":null},{"id":"2502.00837","title":"Explainability in Practice: A Survey of Explainable NLP Across Various Domains","created":"2025-02-02","cited_by_count":11,"area":null},{"id":"2201.05177","title":"Making a (Counterfactual) Difference One Rationale at a Time","created":"2022-01-13","cited_by_count":10,"area":null},{"id":"2305.12710","title":"Beyond Labels: Empowering Human Annotators with Natural Language Explanations through a Novel Active-Learning Architecture","created":"2023-05-22","cited_by_count":10,"area":null},{"id":"2308.14272","title":"Goodhart's Law Applies to NLP's Explanation Benchmarks","created":"2023-08-28","cited_by_count":10,"area":null},{"id":"2206.00759","title":"Interpretability Guarantees with Merlin-Arthur Classifiers","created":"2022-06-01","cited_by_count":9,"area":null},{"id":"2402.02933","title":"Intrinsic User-Centric Interpretability through Global Mixture of Experts","created":"2024-02-05","cited_by_count":9,"area":"moe"},{"id":"2106.01809","title":"Exploring Distantly-Labeled Rationales in Neural Network Models","created":"2021-06-03","cited_by_count":8,"area":null},{"id":"2203.08788","title":"Are Shortest Rationales the Best Explanations for Human Understanding?","created":"2022-03-16","cited_by_count":8,"area":null},{"id":"2403.07955","title":"Towards Faithful Explanations: Boosting Rationalization with Shortcuts Discovery","created":"2024-03-12","cited_by_count":8,"area":null},{"id":"2310.16607","title":"On the Interplay between Fairness and Explainability","created":"2023-10-25","cited_by_count":7,"area":null},{"id":"2410.15268","title":"GraphNarrator: Generating Textual Explanations for Graph Neural Networks","created":"2024-10-20","cited_by_count":7,"area":null},{"id":"2109.03756","title":"Diagnostics-Guided Explanation Generation","created":"2021-09-08","cited_by_count":6,"area":null},{"id":"2503.06202","title":"Breaking Free from MMI: A New Frontier in Rationalization by Probing Input Utilization","created":"2025-03-08","cited_by_count":6,"area":null},{"id":"2505.02118","title":"Adversarial Cooperative Rationalization: The Risk of Spurious Correlations in Even Clean Datasets","created":"2025-05-04","cited_by_count":6,"area":null},{"id":"2110.02056","title":"Are Training Resources Insufficient? Predict First Then Explain!","created":"2021-08-29","cited_by_count":5,"area":null},{"id":"2402.09702","title":"Sparse and Faithful Explanations Without Sparse Models","created":"2024-02-15","cited_by_count":5,"area":null},{"id":"2407.19308","title":"Comprehensive Attribution: Inherently Explainable Vision Model with Feature Detector","created":"2024-07-27","cited_by_count":5,"area":null},{"id":"2601.20815","title":"GNN Explanations that do not Explain and How to find Them","created":"2026-01-28","cited_by_count":5,"area":null},{"id":"2207.13948","title":"An Interpretability Evaluation Benchmark for Pre-trained Language Models","created":"2022-07-28","cited_by_count":4,"area":null},{"id":"2212.09603","title":"Explanation Regeneration via Information Bottleneck","created":"2022-12-19","cited_by_count":4,"area":null},{"id":"2301.06009","title":"Rationalizing Predictions by Adversarial Information Calibration","created":"2023-01-15","cited_by_count":4,"area":null},{"id":"2310.14418","title":"REFER: An End-to-end Rationale Extraction Framework for Explanation Regularization","created":"2023-10-22","cited_by_count":4,"area":null},{"id":"2410.03531","title":"MARE: Multi-Aspect Rationale Extractor on Unsupervised Rationale Extraction","created":"2024-10-04","cited_by_count":4,"area":null},{"id":"2111.09029","title":"Towards Interpretable and Reliable Reading Comprehension: A Pipeline Model with Unanswerability Prediction","created":"2021-11-17","cited_by_count":3,"area":null},{"id":"2305.17534","title":"Unsupervised Selective Rationalization with Noise Injection","created":"2023-05-27","cited_by_count":3,"area":null},{"id":"2402.17914","title":"Extracting Lexical Features from Dialects via Interpretable Dialect Classifiers","created":"2024-02-27","cited_by_count":3,"area":null},{"id":"2506.05586","title":"CoFrNets: Interpretable Neural Architecture Inspired by Continued Fractions","created":"2025-06-05","cited_by_count":3,"area":null},{"id":"2206.02761","title":"Dual Decomposition of Convex Optimization Layers for Consistent Attention in Medical Images","created":"2022-06-06","cited_by_count":2,"area":null},{"id":"2210.04145","title":"Fine-grained Anomaly Detection in Sequential Data via Counterfactual Explanations","created":"2022-10-09","cited_by_count":2,"area":null},{"id":"2304.08174","title":"Benchmarking Faithfulness: Towards Accurate Natural Language Explanations in Vision-Language Tasks","created":"2023-04-03","cited_by_count":2,"area":null},{"id":"2310.18827","title":"All Things Considered: Detecting Partisan Events from News Media with Cross-Article Comparison","created":"2023-10-28","cited_by_count":2,"area":null},{"id":"2311.02344","title":"You Only Forward Once: Prediction and Rationalization in A Single Forward Pass","created":"2023-11-04","cited_by_count":2,"area":null},{"id":"2501.03182","title":"Boosting Explainability through Selective Rationalization in Pre-trained Language Models","created":"2025-01-03","cited_by_count":2,"area":null},{"id":"2512.05373","title":"Text Rationalization for Robust Causal Effect Estimation","created":"2025-12-05","cited_by_count":2,"area":null},{"id":"2205.06756","title":"Interlock-Free Multi-Aspect Rationalization for Text Classification","created":"2022-05-13","cited_by_count":1,"area":null},{"id":"2306.00652","title":"Explanation Graph Generation via Generative Pre-training over Synthetic Graphs","created":"2023-06-01","cited_by_count":1,"area":"lr"},{"id":"2408.10795","title":"Adversarial Attack for Explanation Robustness of Rationalization Models","created":"2024-08-20","cited_by_count":1,"area":null},{"id":"2508.11388","title":"Model Interpretability and Rationale Extraction by Input Mask Optimization","created":"2025-08-15","cited_by_count":1,"area":null},{"id":"2510.13393","title":"Learnable Game-theoretic Policy Optimization for Data-centric Self-explanation Rationalization","created":"2025-10-15","cited_by_count":1,"area":null},{"id":"2511.16353","title":"Learning from Sufficient Rationales: Analysing the Relationship Between Explanation Faithfulness and Token-level Regularisation Strategies","created":"2025-11-20","cited_by_count":1,"area":null},{"id":"2605.12575","title":"Are Compact Rationales Free? Measuring Tile Selection Headroom in Frozen WSI-MIL","created":"2026-05-12","cited_by_count":1,"area":null},{"id":"2304.01638","title":"Multidimensional Perceptron for Efficient and Explainable Long Text Classification","created":"2023-04-04","cited_by_count":0,"area":null},{"id":"2306.04505","title":"Hardness of Deceptive Certificate Selection","created":"2023-06-07","cited_by_count":0,"area":null},{"id":"2410.12511","title":"Advancing Fairness in Natural Language Processing: From Traditional Methods to Explainability","created":"2024-10-16","cited_by_count":0,"area":null},{"id":"2410.20483","title":"Improving Decision Sparsity","created":"2024-10-27","cited_by_count":0,"area":null},{"id":"2507.21302","title":"Can human clinical rationales improve the performance and explainability of clinical text classification models?","created":"2025-07-28","cited_by_count":0,"area":null},{"id":"2512.11108","title":"Explanation Bias is a Product: Revealing the Hidden Lexical and Position Preferences in Post-Hoc Feature Attribution","created":"2025-12-11","cited_by_count":0,"area":null},{"id":"2601.12419","title":"Legal Experts Disagree With Rationale Extraction Techniques for Explaining ECtHR Case Outcome Classification","created":"2026-01-18","cited_by_count":0,"area":null},{"id":"2606.28615","title":"What LLMs explain is not what they believe: Evaluating explanation sufficiency under models' own input beliefs","created":"2026-06-26","cited_by_count":0,"area":"lr"},{"id":"2609.36984","title":"REALHOP: Rethinking Multi-Hop Reasoning Evaluation via Behavioral Auditing","created":"2026-09-29","cited_by_count":0,"area":"lr"}],"next_cursor":null}