[36] Eliza Kosoy, David M Chan, Adrian Liu, Jasmine Collins, Bryanna Kaufmann, Sandy Han Huang, Jessica B Hamrick, John Canny, Nan Rosemary Ke, and Alison Gopnik. Towards understanding how machines can learn causal overhypotheses. arXiv preprint arXiv:2206.08353, 2022.
[37] Chayakrit Krittanawong, Ameesh Isath, Robert S Rosenson, Muzamil Khawaja, Zhen Wang, Sonya E Fogg, Salim S Virani, Lu Qi, Yin Cao, Michelle T Long, et al. Alcohol consumption and cardiovascular health. The American Journal of Medicine, 135(10):1213-1230, 2022.
[38] Aviral Kumar, Justin Fu, Matthew Soh, George Tucker, and Sergey Levine. Stabilizing off-policy q-learning via bootstrapping error reduction. Advances in Neural Information Processing Systems, 32, 2019.
[39] Brenden M Lake, Tomer D Ullman, Joshua B Tenenbaum, and Samuel J Gershman. Building machines that learn and think like people. Behavioral and brain sciences, 40, 2017.
[40] Andrew K Lampinen, Ishita Dasgupta, Stephanie CY Chan, Kory Matthewson, Michael Henry Tessler, Antonia Creswell, James L McClelland, Jane X Wang, and Felix Hill. Can language models learn from explanations in context? Findings of EMNLP, 2022.
[41] Andrew K Lampinen, Nicholas Roy, Ishita Dasgupta, Stephanie CY Chan, Allison Tam, James Mcclelland, Chen Yan, Adam Santoro, Neil C Rabinowitz, Jane Wang, et al. Tell me why! explanations support learning relational and causal structure. In International Conference on Machine Learning, pages 11868-11890. PMLR, 2022.
[42] Michael Laskin, Luyu Wang, Junhyuk Oh, Emilio Parisotto, Stephen Spencer, Richie Steigerwald, DJ Strouse, Steven Hansen, Angelos Filos, Ethan Brooks, et al. In-context reinforcement learning with algorithm distillation. arXiv preprint arXiv:2210.14215, 2022.
[43] Belinda Z Li, Maxwell Nye, and Jacob Andreas. Implicit representations of meaning in neural language models. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 1813–1827, 2021.
[44] Kenneth Li, Aspen K Hopkins, David Bau, Fernanda Viégas, Hanspeter Pfister, and Martin Wattenberg. Emergent world representations: Exploring a sequence model trained on a synthetic task. arXiv preprint arXiv:2210.13382, 2022.
[45] Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig. Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing. ACM Comput. Surv., 55(9), jan 2023. ISSN 0360-0300. doi: 10.1145/3560815. URL https://doi.org/10.1145/3560815.
[46] Tania Lombrozo. The structure and function of explanations. Trends in cognitive sciences, 10 (10):464-470, 2006.
[47] Tania Lombrozo and Susan Carey. Functional explanation and the function of explanation. Cognition, 99(2):167-204, 2006.
[48] Andrew L Maas, Awni Y Hannun, Andrew Y Ng, et al. Rectifier nonlinearities improve neural network acoustic models. In Proc. icml, volume 30, page 3. Atlanta, Georgia, USA, 2013.
[49] Douglas B Markant and Todd M Gureckis. Is it better to select or to receive? learning via active and passive hypothesis testing. Journal of Experimental Psychology: General, 143(1):94, 2014.
[50] Suraj Nair, Yuke Zhu, Silvio Savarese, and Li Fei-Fei. Causal induction from visual observations for goal directed tasks. arXiv preprint arXiv:1910.01751, 2019.
[51] Andrew Nam, Christopher Hughes, Thomas Icard, and Tobias Gerstenberg. Show and tell: Learning causal structures from observations and explanations.
[52] Sharan Narang, Colin Raffel, Katherine Lee, Adam Roberts, Noah Fiedel, and Karishma Malkan. WT5?! training text-to-text models to explain their predictions. arXiv preprint arXiv:2004.14546, 2020.