[1] Tom Brown et al. "Language models are few-shot learners". In: Advances in neural information processing systems 33 (2020), pp. 1877-1901.
[2] Taylor Webb, Keith J Holyoak, and Hongjing Lu. "Emergent Analogical Reasoning in Large Language Models". In: arXiv preprint arXiv:2212.09196 (2022).
[3] Sang Michael Xie et al. "An explanation of in-context learning as implicit bayesian inference". In: arXiv preprint arXiv:2111.02080 (2021).
[4] Damai Dai et al. "Why Can GPT Learn In-Context? Language Models Secretly Perform Gradient Descent as Meta Optimizers". In: arXiv preprint arXiv:2212.10559 (2022).
[5] Catherine Olsson et al. "In-context learning and induction heads". In: arXiv preprint arXiv:2209.11895 (2022).
[6] Dileep George et al. "Clone-structured graph representations enable flexible learning and vicarious evaluation of cognitive maps". In: Nature communications 12.1 (2021), p. 2392.
[7] Antoine Dedieu et al. "Learning higher-order sequential structure with cloned HMMs". In: arXiv preprint arXiv:1905.00507 (2019).
[8] Murray Shanahan and Melanie Mitchell. "Abstraction for Deep Reinforcement Learning". In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22. 2022, pp. 5588-5596.
[9] Neel Nanda et al. "Progress measures for grokking via mechanistic interpretability". In: arXiv preprint arXiv:2301.05217 (2023).
[10] Kevin Meng et al. "Locating and Editing Factual Associations in GPT". In: Advances in Neural Information Processing Systems 36 (2022).
[11] Ashish Vaswani et al. "Attention is all you need". In: Advances in neural information processing systems 30 (2017).
[12] Lonnie Chrisman. "Reinforcement learning with perceptual aliasing: The perceptual distinctions approach". In: AAAI. Vol. 1992. CiteSeer. 1992, pp. 183-188.
[13] Judea Pearl. Causality. Cambridge university press, 2009.
[14] Vatsal Sharan et al. "Learning overcomplete hmms". In: Advances in Neural Information Processing Systems 30 (2017).
[15] J Swaroop Guntupalli et al. "Graph schemas as abstractions for transfer learning, inference, and planning". In: arXiv preprint arXiv:2302.07350 (2023).
[16] Jonas Peters, Dominik Janzing, and Bernhard Schölkopf. Elements of causal inference: foundations and learning algorithms. The MIT Press, 2017.
[17] Daniel Eaton and Kevin Murphy. "Exact Bayesian structure learning from uncertain interventions". In: Artificial intelligence and statistics. PMLR. 2007, pp. 107-114.
[18] Arthur P Dempster, Nan M Laird, and Donald B Rubin. "Maximum likelihood from incomplete data via the EM algorithm". In: Journal of the royal statistical society: series B (methodological) 39.1 (1977), pp. 1-22.
[19] Judea Pearl. Probabilistic reasoning in intelligent systems: networks of plausible inference. Morgan kaufmann, 1988.
[20] Bingbin Liu et al. "Transformers learn shortcuts to automata". In: arXiv preprint arXiv:2210.10749 (2022).
[21] Oren Kolodny, Arnon Lotem, and Shimon Edelman. "Learning a generative probabilistic grammar of experience: A process-level model of language acquisition". In: Cognitive Science 39.2 (2015), pp. 227-267.
[22] Zoubin Ghahramani and Michael Jordan. "Factorial hidden Markov models". In: Advances in Neural Information Processing Systems 8 (1995).
[23] Frederick Jelinek. "Continuous speech recognition by statistical methods". In: Proceedings of the IEEE 64.4 (1976), pp. 532-556.