[14] Marcos Treviso and André FT Martins. The explanation game: Towards prediction explainability through sparse communication. In Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP, pages 107-118, 2020. URL https://arxiv.org/abs/2004.13876.
[15] Danish Pruthi, Rachit Bansal, Bhuwan Dhingra, Livio Baldini Soares, Michael Collins, Zachary C Lipton, Graham Neubig, and William W Cohen. Evaluating explanations: How much do explanations from the teacher aid students? Transactions of the Association for Computational Linguistics, 10:359–375, 2022. URL https://arxiv.org/abs/2012.00893.
[16] Peter Hase and Mohit Bansal. Evaluating explainable ai: Which algorithmic explanations help users predict model behavior? In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 5540-5552, 2020. URL https://arxiv.org/abs/2005.01831.
[17] Peter Hase, Shiyue Zhang, Harry Xie, and Mohit Bansal. Leakage-adjusted simulatability: Can models generate non-trivial explanations of their behavior in natural language? arXiv preprint arXiv:2010.04119, 2020. URL https://arxiv.org/abs/2010.04119.
[18] Finale Doshi-Velez and Been Kim. Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv:1702.08608, 2017. URL https://arxiv.org/abs/1702.08608.
[19] Zachary C Lipton. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue, 16(3):31-57, 2018. URL https://dl.acm.org/doi/10.1145/3236386.3241340.
[20] Andrew Lampinen, Ishita Dasgupta, Stephanie Chan, Kory Mathewson, Mh Tessler, Antonia Creswell, James McClelland, Jane Wang, and Felix Hill. Can language models learn from explanations in context? In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 537-563, Abu Dhabi, United Arab Emirates, December 2022. Association for Computational Linguistics. URL https://aclanthology.org/2022-findings-emnlp.38.
[21] Jiaqi Ma, Dylan Slack, Asma Ghandeharioun, Sameer Singh, Himabindu Lakkaraju, et al. Post hoc explanations of language models can improve language models. arXiv preprint arXiv:2305.11426, 2023. URL https://arxiv.org/abs/2305.11426.
[22] Aman Madaan, Niket Tandon, Prakhar Gupta, Skyler Hallinan, Luyu Gao, Sarah Wiegreffe, Uri Alon, Nouha Dziri, Shrimai Prabhumoye, Yiming Yang, et al. Self-refine: Iterative refinement with self-feedback. arXiv preprint arXiv:2303.17651, 2023. URL https://arxiv.org/abs/2303.17651.
[23] Geoffrey Hinton, Oriol Vinyals, and Jeff Dean. Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531, 2015. URL https://arxiv.org/abs/1503.02531.
[24] Aaron Chan, Zhiyuan Zeng, Wyatt Lake, Brihi Joshi, Hanjie Chen, and Xiang Ren. Knife: Knowledge distillation with free-text rationales. arXiv preprint arXiv:2212.09721, 2022. URL https://arxiv.org/abs/2212.09721.
[25] Sarah Wiegrefe, Jack Hessel, Swabha Swayamdipta, Mark Riedl, and Yejin Choi. Reframing human-ai collaboration for generating free-text explanations. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 632–658, 2022. URL https://arxiv.org/abs/2112.08674.
[26] Swarnadeep Saha, Peter Hase, Nazneen Rajani, and Mohit Bansal. Are hard examples also harder to explain? a study with human and model-generated explanations. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 2121-2131, Abu Dhabi, United Arab Emirates, December 2022. Association for Computational Linguistics. URL https://arxiv.org/abs/2211.07517.