Measuring Association Between Labels and Free-Text Rationales

Attention Is All You Need

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404

BLEU: a method for automatic evaluation of machine translation

Papineni, K., Roukos, S., Ward, T., & Zhu, W.-J. (2001) · Proceedings of the 40th Annual Meeting on Association for Computational Linguistics - ACL '02 · doi:10.3115/1073083.1073135 · Cited by 21,344

Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer

Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., & Liu, P. J. (2019) · arXiv · doi:10.48550/arxiv.1910.10683 · Cited by 10,929

Transformers: State-of-the-Art Natural Language Processing

Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., Cistac, P., Rault, T., Louf, R., Funtowicz, M., et al. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations · doi:10.18653/v1/2020.emnlp-demos.6 · Cited by 8,741

Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank

Socher, R., Perelygin, A., Wu, J., Chuang, J., Manning, C. D., Ng, A., & Potts, C. (2013) · Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d13-1170 · Cited by 6,878

Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

Simonyan, K., Vedaldi, A., & Zisserman, A. (2013) · arXiv · doi:10.48550/arxiv.1312.6034 · Cited by 5,370

Explanation in artificial intelligence: Insights from the social sciences

Miller, T. (2019) · Artificial Intelligence · doi:10.1016/j.artint.2018.07.007 · Cited by 5,302

A large annotated corpus for learning natural language inference

Bowman, S. R., Angeli, G., Potts, C., & Manning, C. D. (2015) · Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d15-1075 · Cited by 3,377

Towards A Rigorous Science of Interpretable Machine Learning

Doshi-Velez, F., & Kim, B. (2017) · arXiv · doi:10.48550/arxiv.1702.08608 · Cited by 3,121

Learning Important Features Through Propagating Activation Differences

Shrikumar, A., Greenside, P., & Kundaje, A. (2017) · arXiv · doi:10.48550/arxiv.1704.02685 · Cited by 2,761

Honey, I Shrunk the Language Model: Impact of Knowledge Distillation Methods on Performance and Explainability

Hendriks, D., Spitzer, P., Kühl, N., & Satzger, G. (2026) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2026.3671872 · Cited by 3

On Advances in Text Generation from Images Beyond Captioning: A Case Study in Self-Rationalization

Palaskar, S., Bhagia, A., Bisk, Y., Metze, F., Black, A. W., & Marasović, A. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.194 · Cited by 4

Proceedings of the 3rd Workshop on Figurative Language Processing (FLP)

(2022) · doi:10.18653/v1/2022.flp-1 · Cited by 3

On Measuring Faithfulness or Self-consistency of Natural Language Explanations

Parcalabescu, L., & Frank, A. (2023) · arXiv · doi:10.48550/arxiv.2311.07466 · Cited by 27

Multi-View Cooperative Learning with Invariant Rationale for Document-Level Relation Extraction

Lin, R., Fan, J., He, Y., Yang, Y., Li, J., & Guo, C. (2024) · Cognitive Computation · doi:10.1007/s12559-024-10322-z · Cited by 2

DoME: An Architecture for Domain Model Evolution at Runtime Using NLP

Gomes, A. M., & Maia, P. H. M. (2023) · Brazilian Symposium on Software Engineering · doi:10.1145/3613372.3613405 · Cited by 2

Black-Box Attacks on Image Activity Prediction and its Natural Language Explanations

Baia, A. E., Poggioni, V., & Cavallaro, A. (2023) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw60793.2023.00396 · Cited by 2

Few Shot Rationale Generation using Self-Training with Dual Teachers

Veerubhotla, A. S., Poddar, L., Yin, J., Szarvas, G., & Eswaran, S. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.297 · Cited by 3

Make Your Decision Convincing! A Unified Two-Stage Framework: Self-Attribution and Decision-Making

Du, Y., Zhao, S., Wang, H., Chen, Y., Bai, R., Qiang, Z., Cai, M., & Qin, B. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.78 · Cited by 2

You Can Do Better! If You Elaborate the Reason When Making PredictionPreprint

Li, D., Tao, J., Chen, Q., & Hu, B. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.14919 · Cited by 3

The Narrative AI Advantage? A Field Experiment on AI-Augmented Evaluations of Early-Stage InnovationsPreprint

Lane, J. N., Boussioux, L., Ayoubi, C., Chen, Y. H., Lin, C., Spens, R., Wagh, P., & Wang, P.-H. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4914367 · Cited by 2

Faithful Serum: Mitigating the Faithfulness Gap in Textual Explanations of LLM Decisions via Attribution Guidance

Alon, B., Zimerman, I., & Wolf, L. (2026) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2026.acl-long.300 · Cited by 1

Feature Attribution Explanations of Session-Based Recommendations

Bruun, S. B., Maistro, M., & Lioma, C. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-88711-6_4 · Cited by 1

Automated Assessment of Fidelity and Interpretability: An Evaluation Framework for Large Language Models’ Explanations (Student Abstract)

Kuo, M.-T., Hsueh, C.-C., & Tsai, R. T. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i21.30470 · Cited by 1

A Roadmap on Developing a Taxonomy for Text Data Mining

Pokusajev, S., & Stefanovič, P. (2024) · IEEE Open Conference of Electrical, Electronic and Information Sciences (eStream) · doi:10.1109/estream61684.2024.10542606 · Cited by 1

Cross-Examiner: Evaluating Consistency of Large Language Model-Generated Explanations

Villa, D., Chang, M., Murugesan, K., Uceda‐Sosa, R., & Ramamurthy, K. N. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11462075 · Cited by 1

GIER: Gap-Driven Self-Refinement for Large Language Models

Dewri, R. (2025) · arXiv · doi:10.48550/arxiv.2509.00325

Proceedings of the First Workshop on Learning with Natural Language Supervision

(2022) · doi:10.18653/v1/2022.lnls-1

Diagnostics-Guided Explanation GenerationPreprint

Atanasova, P., Simonsen, J. G., Lioma, C., & Augenstein, I. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.03756

The model thinks what?! Interpreting deep NLP models with rationales and influence

Jain, S. (2022) · Digital Repository Service (Northeastern University) · doi:10.17760/d20467212

Explanation Graph Generation via Generative Pre-training over Synthetic Graphs

Cui, H., Li, S., Zhang, Y., & Shi, Q. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.629

Executive Summary

Atanasova, P. (2024) · Springer eBooks · doi:10.1007/978-3-031-51518-7_1

From Information Overload to Intelligent Recommendation: The Power of Large Language Models

Sahni, D. K., Khurana, D., & Jain, R. (2024) · International Conference on Advancement in Electronics & Communication Engineering (AECE) · doi:10.1109/aece62803.2024.10911816

Towards Explainable Medical Machine Reading Comprehension With Rationale Generation

Li, D., He, S., Hu, B., & Chen, Q. (2025) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/taslpro.2025.3555105

Self-explaining Neural Network for Multi-criteria Sentiment Analysis

Hoang, X.-T., Nguyen, V.-L., Kitagawa, T., & Huynh, V. (2025) · Lecture notes in computer science · doi:10.1007/978-981-96-4606-7_8

Combating disinformation with explainable and efficient fact-checking

Yang, J. (2024) · doi:10.47749/t/unicamp.2024.1456447

Iterative Self-Improvement of Vision Language Models for Image Scoring and Self-Explanation

Tanji, N., & Yamasaki, T. (2025) · Proceedings - International Conference on Image Processing · doi:10.1109/icip55913.2025.11084643

Believing without Seeing: Quality Scores for Contextualizing Vision-Language Model Explanations

He, K., Srinivasan, T., Joshi, B., Ren, X. Y., Thomason, J., & Swayamdipta, S. (2026) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2026.acl-long.673

Developing trustworthy language models with consistent predictions

Jang, M. (2024) · Oxford University Research Archive (ORA) (University of Oxford) · doi:10.5287/ora-pm75v7jex

Interpretable and Controllable Language Models

Hase, P. (2024) · Carolina Digital Repository (University of North Carolina at Chapel Hill) · doi:10.17615/dvq0-fp08

Towards reliability and interactive debugging for large language models

Paranjape, B. (2024) · ResearchWorks at the University of Washington (University of Washington)

Hierarchical Deep Learning Framework Integrating Structural Interaction Potentials and Evolutionary Information for Protein-Protein Interaction Affinity PredictionPreprint

Ibrahim, A. F., Hassan, M., & Ali, Y. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.01.12.698948

MREF: Metric-Based Instance Re-Weighting for Rationale Enhancement

Huang, Y., Kuang, Z., Chiang, M.-F., & Lee, W. C. (2025) · Proceedings · doi:10.1109/icdm65498.2025.00042

GEESE: Generating and Evaluating Explanations for Semantic Entailment: A CALAMITA Challenge

Zaninello, A., & Magnini, B. (2024) · Open Research (University of Surrey)

The Power of Trust in Recommender Systems: Improving Outcomes and Reducing Noise

Sahni, D. K., Khurana, D., Nakul, & Rani, S. (2025) · Global AI Summit - International Conference on Artificial Intelligence and Emerging Technology (AI Summit) · doi:10.1109/aisummit66170.2025.11411632

Feedback-Integrated Short Answer Grading with Data Augmentation and Large Language Models

Bin, Z., Chen, Z., & Liu, S. (2025) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata66926.2025.11402058

Knowledge Modeling and Multilingual Information Extraction for the Understanding of the Cultural Heritage of Silk

Schleider, T. (2022) · theses.fr (ABES) · doi:10.70675/6f2b45eez5308z4992z9e4azd2a0ac72f19d

Making AI Understandable for Humans : the Plausibility of Attention-Based Explanations in Natural Language Processing

Nguyen, D. H. (2024) · theses.fr (ABES) · doi:10.70675/97b1b150zc009z4f96z9ec6z2790870387a0

Rank-ICL: Ranking-based In-context Learning for Search Result Explanation

Laksito, A. D., Alqarni, A. A., & Stevenson, M. (2026) · ACM SIGIR International Conference on Theory of Information Retrieval (ICTIR) · doi:10.1145/3805713.3820420

Beyond Accuracy: A Mixed-Methods Audit of Chain-of-Thought Failures in LLM-Based COVID-19 Vaccine Stance Detection

Praschk, A., Fischill-Neudeck, V., Caspari, T., & Wiesinger, H. (2026) · Journal of Medical Systems · doi:10.1007/s10916-026-02449-3

GRAD: Graph-Based Rationale-Aware Distillation With Prototype Contrastive Learning for Korean Short-Text Classification

Yoo, J., Kim, B., Yang, Y., & Jang, H. (2026) · IEEE Access · doi:10.1109/access.2026.3725783

GRACE: Gradient-Regulated Approach for Consistent Explanations

Basu, S. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-36039-7_33

Evidence-Consistent Generative Detection under Scenario-Level Distribution Shift

Kim, S., & Bak, J. (2026) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3799682.3840763

Prompt Escalation for Lightweight Large Language Models: An Empirical Evaluation of Cost–Performance Trade-Offs

Kim, S., & Ko, B. (2026) · Applied Sciences · doi:10.3390/app16189190

Faithfulness in Black-Box LLM-Based Recommender Systems’ Explanations

Silva, Í. D. S., & Marinho, L. B. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-39892-5_45

Task Format as a Factor in Adaptation: A Controlled Comparison of LoRA and Full Fine-Tuning for Prompt-Safety ClassificationPreprint

Alderete, J. D., Betageri, S., Flynn, J., Greenhalgh, A., Rubin, J., & Annam, Y. V. (2026) · arXiv (Cornell University) · doi:10.48550/arxiv.2610.33813

Better Answers, Weaker Evidence Control: Evidence Bypass in On-Policy DistillationPreprint

Xu, R., Zhao, Y., Chen, Z., Kang, X., & Cohan, A. (2026) · arXiv (Cornell University) · doi:10.48550/arxiv.2611.01967

Human Ratings Do Not Reflect Downstream Utility: A Study of Free-Text Explanations for Model Predictions

Kunz, J., Jirenius, M., Holmström, O., & Kuhlmann, M. (2022) · BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP (BlackboxNLP) · doi:10.18653/v1/2022.blackboxnlp-1.14 · Cited by 4

Investigating the Benefits of Free-Form Rationales

Sun, J., Swayamdipta, S., May, J., & Ma, X. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.432 · Cited by 23

Flexible Instance-Specific Rationalization of NLP Models

Chrysostomou, G., & Αλέτρας, Ν. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i10.21298 · Cited by 14

Plausible Extractive Rationalization through Semi-Supervised Entailment Signal

Jie, Y. W., Satapathy, R., & Cambria, E. (2024) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2024.findings-acl.307 · Cited by 8

Rationale-Inspired Natural Language Explanations with Commonsense.Preprint

Majumder, B. P., Camburu, O.-M., Lukasiewicz, T., & McAuley, J. (2021) · arXiv (Cornell University) · Cited by 16