{"total":48,"rows":[{"id":"2206.06336","title":"Language Models are General-Purpose Interfaces","created":"2022-06-13","cited_by_count":97,"area":null},{"id":"2307.08678","title":"Do Models Explain Themselves? Counterfactual Simulatability of Natural Language Explanations","created":"2023-07-17","cited_by_count":85,"area":null},{"id":"2105.03761","title":"e-ViL: A Dataset and Benchmark for Natural Language Explanations in Vision-Language Tasks","created":"2021-05-08","cited_by_count":84,"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":"2203.05081","title":"NLX-GPT: A Model for Natural Language Explanations in Vision and Vision-Language Tasks","created":"2022-03-09","cited_by_count":60,"area":null},{"id":"2105.07878","title":"A Review on Explainability in Multimodal Deep Neural Nets","created":"2021-05-17","cited_by_count":46,"area":null},{"id":"2103.16561","title":"Diagnosing Vision-and-Language Navigation: What Really Matters","created":"2021-03-30","cited_by_count":36,"area":null},{"id":"2207.00056","title":"MultiViz: Towards Visualizing and Understanding Multimodal Models","created":"2022-06-30","cited_by_count":36,"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":"2207.10285","title":"Grounding Visual Representations with Texts for Domain Generalization","created":"2022-07-21","cited_by_count":30,"area":null},{"id":"2306.05731","title":"Multimodal Explainable Artificial Intelligence: A Comprehensive Review of Methodological Advances and Future Research Directions","created":"2023-06-09","cited_by_count":25,"area":null},{"id":"2106.13876","title":"Knowledge-Grounded Self-Rationalization via Extractive and Natural Language Explanations","created":"2021-06-25","cited_by_count":24,"area":null},{"id":"2404.18624","title":"Do Vision & Language Decoders use Images and Text equally? How Self-consistent are their Explanations?","created":"2024-04-29","cited_by_count":24,"area":null},{"id":"2104.04515","title":"Connecting Attributions and QA Model Behavior on Realistic Counterfactuals","created":"2021-04-09","cited_by_count":20,"area":null},{"id":"2408.02788","title":"GazeXplain: Learning to Predict Natural Language Explanations of Visual Scanpaths","created":"2024-08-05","cited_by_count":15,"area":null},{"id":"2105.02626","title":"A First Look: Towards Explainable TextVQA Models via Visual and Textual Explanations","created":"2021-04-29","cited_by_count":14,"area":null},{"id":"2105.09787","title":"Explainable Activity Recognition for Smart Home Systems","created":"2021-05-20","cited_by_count":14,"area":null},{"id":"2203.06107","title":"REX: Reasoning-aware and Grounded Explanation","created":"2022-03-11","cited_by_count":12,"area":null},{"id":"2308.09033","title":"Uni-NLX: Unifying Textual Explanations for Vision and Vision-Language Tasks","created":"2023-08-17","cited_by_count":12,"area":null},{"id":"2204.04601","title":"Explaining Deep Convolutional Neural Networks via Latent Visual-Semantic Filter Attention","created":"2022-04-10","cited_by_count":11,"area":null},{"id":"2305.14770","title":"Using Natural Language Explanations to Rescale Human Judgments","created":"2023-05-24","cited_by_count":11,"area":null},{"id":"2207.00779","title":"FRAME: Evaluating Rationale-Label Consistency Metrics for Free-Text Rationales","created":"2022-07-02","cited_by_count":10,"area":null},{"id":"2309.02155","title":"S3C: Semi-Supervised VQA Natural Language Explanation via Self-Critical Learning","created":"2023-09-05","cited_by_count":10,"area":null},{"id":"2504.13275","title":"ChartQA-X: Generating Explanations for Visual Chart Reasoning","created":"2025-04-17","cited_by_count":10,"area":null},{"id":"2411.11150","title":"A Comprehensive Survey on Visual Question Answering Datasets and Algorithms","created":"2024-11-17","cited_by_count":8,"area":null},{"id":"2407.08669","title":"Segmentation-guided Attention for Visual Question Answering from Remote Sensing Images","created":"2024-07-11","cited_by_count":7,"area":null},{"id":"2407.20899","title":"Faithful and Plausible Natural Language Explanations for Image Classification: A Pipeline Approach","created":"2024-07-30","cited_by_count":7,"area":null},{"id":"2212.04231","title":"Harnessing the Power of Multi-Task Pretraining for Ground-Truth Level Natural Language Explanations","created":"2022-12-08","cited_by_count":6,"area":null},{"id":"2406.19170","title":"The Illusion of Competence: Evaluating the Effect of Explanations on Users' Mental Models of Visual Question Answering Systems","created":"2024-06-27","cited_by_count":6,"area":null},{"id":"2312.13594","title":"Towards More Faithful Natural Language Explanation Using Multi-Level Contrastive Learning in VQA","created":"2023-12-21","cited_by_count":5,"area":null},{"id":"2301.10799","title":"Towards a Unified Model for Generating Answers and Explanations in Visual Question Answering","created":"2023-01-25","cited_by_count":3,"area":null},{"id":"2311.05043","title":"Zero-shot Translation of Attention Patterns in VQA Models to Natural Language","created":"2023-11-08","cited_by_count":3,"area":null},{"id":"2505.19299","title":"A Necessary Step toward Faithfulness: Measuring and Improving Consistency in Free-Text Explanations","created":"2025-05-25","cited_by_count":3,"area":null},{"id":"2605.25052","title":"Faithfulness Metrics Don't Measure Faithfulness: A Meta-Evaluation with Ground Truth","created":"2026-05-24","cited_by_count":3,"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":"2309.01955","title":"A Survey on Interpretable Cross-modal Reasoning","created":"2023-09-05","cited_by_count":2,"area":null},{"id":"2402.03896","title":"Multimodal Rationales for Explainable Visual Question Answering","created":"2024-02-06","cited_by_count":2,"area":null},{"id":"2508.00171","title":"On the Risk of Misleading Reports: Diagnosing Textual Biases in Multimodal Clinical AI","created":"2025-07-31","cited_by_count":2,"area":null},{"id":"2105.05999","title":"Designing Multimodal Datasets for NLP Challenges","created":"2021-05-12","cited_by_count":1,"area":null},{"id":"2310.00503","title":"Black-box Attacks on Image Activity Prediction and its Natural Language Explanations","created":"2023-09-30","cited_by_count":1,"area":null},{"id":"2411.13053","title":"MEGL: Multimodal Explanation-Guided Learning","created":"2024-11-20","cited_by_count":1,"area":null},{"id":"2501.13735","title":"A Study of the Plausibility of Attention between RNN Encoders in Natural Language Inference","created":"2025-01-23","cited_by_count":1,"area":null},{"id":"2508.12430","title":"Adversarial Attacks on VQA-NLE: Exposing and Alleviating Inconsistencies in Visual Question Answering Explanations","created":"2025-08-17","cited_by_count":1,"area":null},{"id":"2104.01290","title":"Measuring Linguistic Diversity During COVID-19","created":"2021-04-03","cited_by_count":0,"area":null},{"id":"2303.04388","title":"Interpretable Visual Question Answering Referring to Outside Knowledge","created":"2023-03-08","cited_by_count":0,"area":null},{"id":"2406.09131","title":"OLGA: One-cLass Graph Autoencoder","created":"2024-06-13","cited_by_count":0,"area":null},{"id":"2507.21246","title":"On Explaining Visual Captioning with Hybrid Markov Logic Networks","created":"2025-07-28","cited_by_count":0,"area":null},{"id":"2603.18752","title":"WeNLEX: Weakly Supervised Natural Language Explanations for Multilabel Chest X-ray Classification","created":"2026-03-19","cited_by_count":0,"area":null}],"next_cursor":null}