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Measuring Association Between Labels and Free-Text Rationales
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In interpretable NLP, we require faithful rationales that reflect the model's decision-making process for an explained instance.While prior work focuses on extractive rationales (a subset of the input words), we investigate their lessstudied counterpart: free-text natural language rationales.We demonstrate that pipelines, models for faithful rationalization on informationextraction style tasks, do not work as well on "reasoning" tasks requiring free-text rationales.We turn to models that jointly predict and rationalize, a class of widely used high-performance models for free-text rationalization.We investigate the extent to which the labels and rationales predicted by these models are associated, a necessary property of faithful explanation.Via two tests, robustness equivalence and feature importance agreement, we find that stateof-the-art T5-based joint models exhibit desirable properties for explaining commonsense question-answering and natural language inference, indicating their potential for producing faithful free-text rationales. 1
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Prior work has shown that even small samples, when selected for conceptual diversity and difficulty, yield robust insights into model behavior.
“While largerscale evaluations are desirable, prior work (Rudin, 2019; Wiegreffe et al., 2021) has shown that even small samples, when selected for conceptual diversity and difficulty, yield robust insights into model behavior.”
From GIER: Gap-Driven Self-Refinement for Large Language Models (2025) · cites this paper as Wiegreffe et al., 2021
Section 4 and the Conclusion describe tests of model behavior across three datasets, but do not address whether small, deliberately selected samples yield robust insights. Searches for small samples, conceptual diversity, difficulty, and sample selection found no passage making that point. (LLM, conf=0.75)
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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
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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
NewsWeeder: Learning to Filter Netnews
Lang, K. (1995) · Elsevier eBooks · doi:10.1016/b978-1-55860-377-6.50048-7 · Cited by 2,119
Hidden factors and hidden topics
McAuley, J., & Leskovec, J. (2013) · ACM Conference on Recommender Systems (RecSys) · doi:10.1145/2507157.2507163 · Cited by 1,565
FEVER: a Large-scale Dataset for Fact Extraction and VERification
Thorne, J., Vlachos, A., Christodoulopoulos, C., & Mittal, A. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) · doi:10.18653/v1/n18-1074 · Cited by 1,343
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Tenney, I., Das, D., & Pavlick, E. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1452 · Cited by 1,307
Jain, S., & Wallace, B. (2019) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n19-1357 · Cited by 1,133
Interpretable Machine Learning
Molnar, C. (2020) · Cited by 1,104
Transformers: State-of-the-Art Natural Language Processing
Wolf, T., Debut, L., Sanh, V., D., M., Châtel, G., Chaumond, J., Rault, T., Voss, C., Wang, F., et al. (2019) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.3385997 · Cited by 1,056
Andreas, J., Rohrbach, M., Darrell, T., & Klein, D. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.12 · Cited by 1,012
Attention is not not Explanation
Wiegreffe, S., & Pinter, Y. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · doi:10.18653/v1/d19-1002 · Cited by 966
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Talmor, A., Herzig, J., Lourie, N., & Berant, J. (2019) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n19-1421 · Cited by 919
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Slack, D., Hilgard, S., Jia, E., Singh, S., & Lakkaraju, H. (2020) · Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society · doi:10.1145/3375627.3375830 · Cited by 900
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Ribeiro, M. T., Wu, T., Guestrin, C., & Singh, S. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.442 · Cited by 890
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(2016) · neural information processing systems · Cited by 556
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“A smaller improvement for E-SNLI could be explained by the fact that E-SNLI has substantially more training examples for each label than CoS-E, which helps a pipeline model learn features predictive of each label.”
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Jain, S., Wiegreffe, S., Pinter, Y., & Wallace, B. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.409 · Cited by 181
Self-citationCorso, G. M. D., Gullì, A., & Romani, F. (2005) · International World Wide Web Conference (WWW) · doi:10.1145/1060745.1060764 · Cited by 173
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Kumar, S., & Talukdar, P. P. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.771 · Cited by 139
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Training Classifiers with Natural Language Explanations
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Ahmed, W. M., Liu, J., Lenz, D., Ghafoor, A., & Robinson, J. P. (2007) · IEEE International Conference on Semantic Computing (ICSC) · doi:10.1109/icosc.2007.4338395 · Cited by 119
AllenNLP Interpret: A Framework for Explaining Predictions of NLP Models
Wallace, E., Tuyls, J., Wang, J., Subramanian, S., Gardner, M., & Singh, S. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations · doi:10.18653/v1/d19-3002 · Cited by 116
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Hase, P., Zhang, S., Xie, H., & Bansal, M. (2020) · Findings of the Association for Computational Linguistics: EMNLP 2020 · doi:10.18653/v1/2020.findings-emnlp.390 · Cited by 62
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Ding, S., Xu, H., & Koehn, P. (2019) · Conference on Machine Translation (WMT) · doi:10.18653/v1/w19-5201 · Cited by 62
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(2010) · Journal of Machine Learning Research · doi:10.5555/1756006.1859912 · Cited by 54
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Evaluating Explanation Methods for Neural Machine Translation
Li, J., Liu, L., Li, H., Li, G., Huang, G., & Shi, S. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.35 · Cited by 27
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Bridging CNNs, RNNs, and Weighted Finite-State Machines
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F1 is Not Enough! Models and Evaluation Towards User-Centered Explainable Question Answering
Schuff, H., Adel, H., & Vu, N. T. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.575 · Cited by 15
Tang, Z. S., Hahn-Powell, G., & Surdeanu, M. (2020) · Annual Meeting of the Association for Computational Linguistics: Student Research Workshop (ACL SRW) · doi:10.18653/v1/2020.acl-srw.23 · Cited by 11
Exploration of Noise Strategies in Semi-supervised Named Entity Classification
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Yeung, A. Y., Joshi, S. D., Williams, J. J., & Rudzicz, F. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.09028 · Cited by 10
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3 of the 88 references couldn’t be matched to a known paper.
Research works printed but not linked: 2
- Miruna-Adriana Clinciu, Arash Eshghi, and Helen Hastie. 2021. A study of automatic metrics for the evaluation of natural language explanations. In Pro- ceedings of the 16th Conference of the European Chapter of the Association for Computational Lin- guistics: Main Volume, pages 2376-2387, Online. Association for Computational Linguistics.
- Omar Zaidan and Jason Eisner. 2008. Modeling an- notators: A generative approach to learning from annotator rationales. In Proceedings of the 2008 Conference on Empirical Methods in Natural Lan- guage Processing, pages 31-40, Honolulu, Hawaii. Association for Computational Linguistics.
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Self-citationTurpin, M., Michael, J., Perez, E., & Bowman, S. R. (2023) · arXiv · doi:10.48550/arxiv.2305.04388 · Cited by 262
Few-Shot Self-Rationalization with Natural Language Prompts
Marasović, A., Beltagy, I., Downey, D., & Peters, M. E. (2022) · Findings of the Association for Computational Linguistics: NAACL · doi:10.18653/v1/2022.findings-naacl.31 · Cited by 87
Self-citationGoing Beyond XAI: A Systematic Survey for Explanation-Guided Learning
Gao, Y., Gu, S., Jiang, J., Hong, S.-S., Yu, D., & Zhao, L. (2024) · ACM Computing Surveys · doi:10.1145/3644073 · Cited by 85
Rationalization for explainable NLP: a survey
Gurrapu, S., Kulkarni, A., Huang, L., Lourentzou, I., & Batarseh, F. A. (2023) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2023.1225093 · Cited by 79
FastIF: Scalable Influence Functions for Efficient Model Interpretation and Debugging
Guo, H., Rajani, N. F., Hase, P., Bansal, M., & Xiong, C. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.808 · Cited by 115
Local Interpretations for Explainable Natural Language Processing: A Survey
Luo, S., Ivison, H., Han, S. C., & Poon, J. (2024) · ACM Computing Surveys · doi:10.1145/3649450 · Cited by 73
Prompting Contrastive Explanations for Commonsense Reasoning Tasks
Paranjape, B., Michael, J., Ghazvininejad, M., Hajishirzi, H., & Zettlemoyer, L. (2021) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2021.findings-acl.366 · Cited by 64
Teach Me to Explain: A Review of Datasets for Explainable NLP.Preprint
Wiegreffe, S., & Marasović, A. (2021) · arXiv (Cornell University) · Cited by 57
Self-citationFLUTE: Figurative Language Understanding through Textual Explanations
Chakrabarty, T., Saakyan, A., Ghosh, D., & Muresan, S. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.481 · Cited by 50
HARE: Explainable Hate Speech Detection with Step-by-Step Reasoning
Yang, Y., Kim, J., Kim, Y., Ho, N., Thorne, J. P., & Yun, S.-Y. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.365 · Cited by 45
SELFEXPLAIN: A Self-Explaining Architecture for Neural Text Classifiers
Rajagopal, D., Balachandran, V., Hovy, E. H., & Tsvetkov, Y. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.64 · Cited by 48
Hase, P., & Bansal, M. (2022) · LNLS · doi:10.18653/v1/2022.lnls-1.4 · Cited by 71
Distilling ChatGPT for Explainable Automated Student Answer Assessment
Li, J., Gui, L., Zhou, Y., West, D., Aloisi, C., & He, Y. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.399 · Cited by 43
Interpreting Deep Learning Models in Natural Language Processing: A ReviewPreprint
Sun, X., Yang, D., Li, X., Zhang, T., Meng, Y., Qiu, H., Wang, G., Hovy, E. H., & Li, J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.10470 · Cited by 60
Tian, Y., Han, Y., Chen, X., Wang, W. W., & Chawla, N. V. (2025) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3701551.3703577 · Cited by 63
A Comprehensive Survey on Self-Interpretable Neural Networks
Ji, Y., Sun, Y., Zhang, Y., Wang, Z., Zhuang, Y., Gong, Z., Shen, D., Qin, C., Zhu, H., & Xiong, H. (2025) · Proceedings of the IEEE · doi:10.1109/jproc.2025.3635153 · Cited by 38
How Interpretable are Reasoning Explanations from Prompting Large Language Models?
Jie, Y. W., Satapathy, R., Goh, R. S. M., & Cambria, E. (2024) · Findings of the Association for Computational Linguistics: NAACL · doi:10.18653/v1/2024.findings-naacl.138 · Cited by 61
Chu, S., Kim, J., Wong, B., & Yi, M. Y. (2025) · Findings of the Association for Computational Linguistics: NAACL · doi:10.18653/v1/2025.findings-naacl.322 · Cited by 37
Filighera, A., Parihar, S., Steuer, T., Meuser, T., & Ochs, S. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.587 · Cited by 33
Faithful Explanations of Black-box NLP Models Using LLM-generated CounterfactualsPreprint
Gat, Y. O., Calderon, N., Feder, A., Chapanin, A., Sharma, A. K., & Reichart, R. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2310.00603 · Cited by 33
Can Rationalization Improve Robustness?
Chen, H., He, J., Narasimhan, K., & Chen, D. (2022) · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2022.naacl-main.278 · Cited by 31
Faithfulness Tests for Natural Language Explanations
Atanasova, P., Camburu, O.-M., Lioma, C., Lukasiewicz, T., Simonsen, J. G., & Augenstein, I. (2023) · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) · doi:10.18653/v1/2023.acl-short.25 · Cited by 69
When Do Program-of-Thought Works for Reasoning?
Bi, Z., Zhang, N., Jiang, Y., Deng, S., Zheng, G., & Chen, H. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i16.29721 · Cited by 31
E-KAR: A Benchmark for Rationalizing Natural Language Analogical Reasoning
Chen, J., Xu, R., Fu, Z., Shi, W., Li, Z., Zhang, X., Sun, C., Li, L., Xiao, Y., & Zhou, H. (2022) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2022.findings-acl.311 · Cited by 30
Self-Evaluation Guided Beam Search for Reasoning
Xie, Y., Kawaguchi, K., Zhao, Y., Zhao, J. X., Kan, M.-Y., He, J., & Xie, M. (2023) · Advances in Neural Information Processing Systems 36 · doi:10.52202/075280-1802 · Cited by 27
Factual and Informative Review Generation for Explainable Recommendation
Xie, Z., Singh, S., McAuley, J., & Majumder, B. P. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i11.26618 · Cited by 23
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
Counterfactual Simulation Training for Chain-of-Thought FaithfulnessPreprint
Hase, P., & Potts, C. (2026) · arXiv (Cornell University) · doi:10.48550/arxiv.2602.20710 · Cited by 22
Diagnosing AI Explanation Methods with Folk Concepts of Behavior
Jacovi, A., Bastings, J., Gehrmann, S., Goldberg, Y., & Filippova, K. (2023) · Journal of Artificial Intelligence Research · doi:10.1145/3593013.3593993 · Cited by 20
FiD-Ex: Improving Sequence-to-Sequence Models for Extractive Rationale Generation
Lakhotia, K., Paranjape, B., Ghoshal, A., Yih, S., Mehdad, Y., & Iyer, S. V. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.301 · Cited by 26
Tang, Z., Agarwal, M., Shypula, A., Wang, B., Wijaya, D. T., Chen, J., & Kim, Y. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.119 · Cited by 19
Schrödinger's tree—On syntax and neural language models
Kulmizev, A., & Nivre, J. (2022) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2022.796788 · Cited by 18
A survey on improving NLP models with human explanations
Hartmann, M., & Sonntag, D. (2022) · doi:10.18653/v1/2022.lnls-1.5 · Cited by 24
Feldhus, N., Hennig, L., Nasert, M. D., Ebert, C., Schwarzenberg, R., & Mller, S. (2023) · doi:10.18653/v1/2023.nlrse-1.4 · Cited by 24
Chan, Y. S., Yong, Z.-X., & Bach, S. H. (2025) · arXiv (Cornell University) · doi:10.48550/arxiv.2507.12428 · Cited by 18
COBRA Frames: Contextual Reasoning about Effects and Harms of Offensive Statements
Zhou, X., Zhu, H., Yerukola, A., Davidson, T., Hwang, J. D., Swayamdipta, S., & Sap, M. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.392 · Cited by 17
REV: Information-Theoretic Evaluation of Free-Text Rationales
Chen, H., Brahman, F., Ren, X., Ji, Y., Choi, Y., & Swayamdipta, S. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.112 · Cited by 29
CH-Wang, S., Saakyan, A., Li, O., Zhou, Y., & Muresan, S. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.215 · Cited by 15
Connecting Attributions and QA Model Behavior on Realistic Counterfactuals
Ye, X., Nair, R., & Durrett, G. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.447 · Cited by 15
Training Language Models with Language Feedback at Scale
Scheurer, J., Campos, J. A., Korbak, T., Chan, J. S., Chen, A., Cho, K., & Perez, E. (2023) · arXiv · doi:10.48550/arxiv.2303.16755 · Cited by 14
Yeo, W. J., Satapathy, R., & Cambria, E. (2025) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2025.emnlp-main.529 · Cited by 14
Diagnostics-Guided Explanation Generation
Atanasova, P., Simonsen, J. G., Lioma, C., & Augenstein, I. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i10.21287 · Cited by 15
An Empirical Study on Explanations in Out-of-Domain Settings
Chrysostomou, G., & Αλέτρας, Ν. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.477 · Cited by 17
Reproducibility in Computational Linguistics: Is Source Code Enough?
Arvan, M., Pina, L., & Parde, N. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.150 · Cited by 14
GraphNarrator: Generating Textual Explanations for Graph Neural Networks
Pan, B., Xiong, Z., Wu, G., Zhang, Z., Zhang, Y., Hu, Y., & Zhao, L. (2025) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2025.acl-long.2 · Cited by 13
Rissanen Data Analysis: Examining Dataset Characteristics via Description LengthPreprint
Perez, E., Kiela, D., & Cho, K. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.03872 · Cited by 13
Few-Shot Out-of-Domain Transfer Learning of Natural Language Explanations in a Label-Abundant Setup
Yordanov, Y., Kocijan, V., Lukasiewicz, T., & Camburu, O.-M. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.255 · Cited by 12
SelfExplain: A Self-Explaining Architecture for Neural Text ClassifiersPreprint
Rajagopal, D., Balachandran, V., Hovy, E. H., & Tsvetkov, Y. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.12279 · Cited by 15
Logical Satisfiability of Counterfactuals for Faithful Explanations in NLI
Sia, S., Belyy, A., Almahairi, A., Khabsa, M., Zettlemoyer, L., & Mathias, L. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i8.26174 · Cited by 11
Are Training Resources Insufficient? Predict First Then Explain!Preprint
Jang, M., & Lukasiewicz, T. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.02056 · Cited by 11
F-Fidelity: A Robust Framework for Faithfulness Evaluation of Explainable AIPreprint
Zheng, X., Shirani, F., Chen, Z., Lin, C., Cheng, W., Guo, W., & Luo, D. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2410.02970 · Cited by 11
ExPUNations: Augmenting Puns with Keywords and Explanations
Sun, J., Narayan-Chen, A., Oraby, S., Cervone, A., Chung, T., Huang, J., Liu, Y., & Peng, N. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.304 · Cited by 10
Can Language Models Laugh at YouTube Short-form Videos?
Ko, D., Lee, S., & Kim, G. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.176 · Cited by 10
Yao, B., Jindal, I., Popa, L. C., Katsis, Y., Ghosh, S., He, L., Lu, Y., Srivastava, S., Li, Y., Hendler, J., et al. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.778 · Cited by 10
Are Latent Reasoning Models Easily Interpretable?Preprint
Dilgren, C., & Wiegreffe, S. (2026) · arXiv (Cornell University) · doi:10.48550/arxiv.2604.04902 · Cited by 10
Self-citationFrom Sycophancy to Deception: A Unified Taxonomy for LLM Spontaneous MisalignmentPreprint
Shi, J., Zhang, T. J., Jin, Z., & Conitzer, V. (2026) · arXiv (Cornell University) · doi:10.48550/arxiv.2604.04788 · Cited by 10
Human-annotated rationales and explainable text classification: a survey
Herrewijnen, E., Nguyen, D., Bex, F., & van Deemter, K. (2024) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2024.1260952 · Cited by 11
SH2: Self-Highlighted Hesitation Helps You Decode More Truthfully
Kai, J., Zhang, T., Hu, H., & Lin, Z. (2024) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2024.findings-emnlp.260 · Cited by 19
FLamE: Few-shot Learning from Natural Language Explanations
Zhou, Y., Zhang, Y., & Tan, C. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.372 · Cited by 12
ZARA: Improving Few-Shot Self-Rationalization for Small Language Models
Chen, W., Yen, A.-Z., Wu, C.-K., Huang, H., & Chen, H. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.310 · Cited by 9
Model Distillation for Faithful Explanations of Medical Code Predictions
Wood-Doughty, Z., Cachola, I. A., & Dredze, M. H. (2022) · Workshop on Biomedical Language Processing (BioNLP) · doi:10.18653/v1/2022.bionlp-1.41 · Cited by 9
Connecting Attributions and QA Model Behavior on Realistic CounterfactualsPreprint
Ye, X., Nair, R., & Durrett, G. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.04515 · Cited by 9
Question Decomposition Improves the Faithfulness of Model-Generated Reasoning
Radhakrishnan, A., Nguyen, K., Chen, A., Chen, C., Denison, C., Hernandez, D., Durmus, E., Hubinger, E., Kernion, J., Lukošiūtė, K., et al. (2023) · arXiv · doi:10.48550/arxiv.2307.11768 · Cited by 8
Does Self-Rationalization Improve Robustness to Spurious Correlations?
Ross, A., Peters, M. E., & Marasović, A. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.501 · Cited by 9
Self-citationLi, D., Hu, B., Chen, Q., & He, S. (2023) · Workshop on Trustworthy Natural Language Processing (TrustNLP) · doi:10.18653/v1/2023.trustnlp-1.1 · Cited by 8
ELAD: Explanation-Guided Large Language Models Active Distillation
Zhang, Y., Pan, B., Chen, L., Hu, Y., & Zhao, L. (2024) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2024.findings-acl.264 · Cited by 14
Improving Language Model Personas via Rationalization with Psychological Scaffolds
Joshi, B., Ren, X., Swayamdipta, S., Koncel-Kedziorski, R., & Paek, T. (2025) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2025.findings-emnlp.1187 · Cited by 8
From outputs to insights: a survey of rationalization approaches for explainable text classification
Guzman, E. M., Schlegel, V., & Batista-Navarro, R. (2024) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2024.1363531 · Cited by 8
Zhang, B., Chen, Q., Zhou, J., Jin, J., & He, L. (2024) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn60899.2024.10651331 · Cited by 7
Yao, B., Sen, P., Popa, L. C., Hendler, J., & Wang, D. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.821 · Cited by 7
Causal Abstraction for Chain-of-Thought Reasoning in Arithmetic Word Problems
Tan, J. (2023) · BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP (BlackboxNLP) · doi:10.18653/v1/2023.blackboxnlp-1.12 · Cited by 7
Cross-Domain Transfer of Generative Explanations Using Text-to-Text Models
Erliksson, K. F., Arpteg, A., Matskin, M., & Payberah, A. H. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-80599-9_8 · Cited by 7
Kunz, J. (2024) · Linköping studies in science and technology. Dissertations · doi:10.3384/9789180754712 · Cited by 7
Self-contradictory reasoning evaluation and detection
Liu, Z., Sanyal, S., Lee, I., Du, Y., Gupta, R. K., Liu, Y., & Zhao, J. (2024) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2024.findings-emnlp.213 · Cited by 6
Self-Critique and Refinement for Faithful Natural Language Explanations
Wang, Y., & Atanasova, P. (2025) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2025.emnlp-main.427 · Cited by 6
Just-DREAM-about-it: Figurative Language Understanding with DREAM-FLUTE
Gu, Y., Fu, Y., Pyatkin, V., Magnusson, I., Mishra, B. D., & Clark, P. A. (2022) · Workshop on Figurative Language Processing (FigLang) · doi:10.18653/v1/2022.flp-1.12 · Cited by 6
CFE2: Counterfactual Editing for Search Result Explanation
Xu, Z., Lamba, H., Ai, Q., Tetreault, J., & Jaimes, A. (2024) · ACM SIGIR International Conference on Theory of Information Retrieval (ICTIR) · doi:10.1145/3664190.3672508 · Cited by 8
SalKG: Learning From Knowledge Graph Explanations for Commonsense ReasoningPreprint
Chan, A., Xu, J., Long, B., Sanyal, S., Gupta, T., & Ren, X. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.08793 · Cited by 6
Evaluating Explanations for Reading Comprehension with Realistic Counterfactuals.Preprint
Ye, X., Nair, R., & Durrett, G. (2021) · arXiv (Cornell University) · Cited by 6
Faithfulness Metrics Don't Measure Faithfulness: A Meta-Evaluation with Ground TruthPreprint
Gur-Arieh, Y., Marasović, A., & Geva, M. (2026) · arXiv (Cornell University) · doi:10.48550/arxiv.2605.25052 · Cited by 6
Self-citationDo Models Explain Themselves? Counterfactual Simulatability of Natural Language Explanations
Chen, Y., Zhong, R., Ri, N., Zhao, C., He, H., Steinhardt, J., Yu, Z., & McKeown, K. (2023) · arXiv · doi:10.48550/arxiv.2307.08678 · Cited by 5
“explanation, the simulation input is the explained input (Hase et al., 2020; Narang et al., 2020; Wiegreffe et al., 2021; Chen et al., 2022; Chan et al., 2022).”
BackgroundLearning to Rationalize for Nonmonotonic Reasoning with Distant Supervision
Brahman, F., Shwartz, V., Rudinger, R., & Choi, Y. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i14.17492 · Cited by 8
Priyadarshana, Y. H. P. P., Liang, Z., & Piumarta, I. (2025) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2025.1564828 · Cited by 5
Aligning What LLMs Do and Say: Towards Self-Consistent Explanations
Admoni, S., Amir, O., Hallak, A., & Ziser, Y. (2026) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2026.findings-acl.49 · Cited by 5
Investigating Self-Rationalizing Models for Commonsense Reasoning
Rancourt, F., Vondrlik, P., Maupomé, D., & Meurs, M. (2023) · Stats · doi:10.3390/stats6030056 · Cited by 5
REFER: An End-to-end Rationale Extraction Framework for Explanation Regularization
Madani, M. R. G., & Minervini, P. (2023) · Conference on Computational Natural Language Learning · doi:10.18653/v1/2023.conll-1.40 · Cited by 5
Siegel, N. Y., Camburu, O.-M., Heess, N., & Perez-Ortiz, M. (2024) · arXiv · doi:10.48550/arxiv.2404.03189 · Cited by 5
“…chose to focus on NLEs in this work because highlight-based explanations are highly restrictive in what they can communicate (Camburu et al., 2021; Wiegreffe et al., 2020), while NLEs allow models to produce justifications that are as expressive as necessary (e.g. they can mention to background…”
BackgroundA Survey of Frontiers in LLM Reasoning: Inference Scaling, Learning to Reason, and Agentic Systems
Ke, Z., Jiao, F., Ming, Y., Nguyen, X.-P., Xu, A., Long, D. X., Li, M., Qin, C., Wang, P., Savarese, S., et al. (2025) · arXiv · doi:10.48550/arxiv.2504.09037 · Cited by 4
Self-Rationalization in the Wild: A Large-scale Out-of-Distribution Evaluation on NLI-related tasks
Yang, J., Glockner, M., Rocha, A., & Gurevych, I. (2025) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00741 · Cited by 4
Jang, M., Majumder, B. P., McAuley, J., Lukasiewicz, T., & Camburu, O.-M. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-short.47 · Cited by 4
Kavumba, P., Brassard, A., Heinzerling, B., & Inui, K. (2023) · Findings of the Association for Computational Linguistics: EACL · doi:10.18653/v1/2023.findings-eacl.162 · Cited by 4
SYNCode: Synergistic Human–LLM Collaboration for Enhanced Data Annotation in Stack Overflow
Xia, M., Maharjan, S., Le, T., Taylor, W., & Song, M. (2025) · Information · doi:10.3390/info16050392 · Cited by 3
Argument-Based Consistency in Toxicity Explanations of LLMs
Mothilal, R. K., Roy, J., Ahmed, S. I., & Guha, S. (2026) · Findings of the Association for Computational Linguistics: EACL · doi:10.18653/v1/2026.findings-eacl.310 · Cited by 3
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
Metadata
- Authors
- Wiegreffe, S., Marasović, A., & Smith, N. A.
- Affiliations
- Georgia Institute of Technology (US)
- Venue
- Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
- Published
- 2021
- Pages
- 10266-10284
- OpenAlex
- W3093530468
- Cited by
- 119
- References
- 88
- Citations by year
- 2026: 9 · 2025: 17 · 2024: 22 · 2023: 29 · 2022: 24 · 2021: 18
- FWCI
- 10.37
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference on Empirical Methods in Natural Language Processing (EMNLP) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Allen Institute for Artificial Intelligence
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Keywords
- interpretability, commonsense question answering, natural language inference, T5
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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