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Language models trained on large text corpora encode rich distributional information about real-world environments and action sequences.
Learning Through Language in Early Childhood
Painter, C · 2005
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Top-down predictions in the cognitive brain
Kveraga, K., Ghuman, A., and Bar, M · 2007
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Sun rgb-d: A rgb-d scene understanding benchmark suite
Song, S., Lichtenberg, S., and Xiao, J · 2015
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Rednet: Residual encoder-decoder network for indoor rgb-d semantic segmentation, 2018
Jiang, J., Zheng, L., Luo, F., and Zhang, Z · 2018
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You that read wrong again! a transposed-word effect in grammaticality judgments
Mirault, J., Snell, J., and Grainger, J · 2018
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Cross-task weakly supervised learning from instructional videos
Zhukov, D., Alayrac, J.-B., Cinbis, R. G., Fouhey, D., Laptev, I., and Sivic, J · 2019
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Automated storytelling via causal, commonsense plot ordering
Ammanabrolu, P., Cheung, W., Broniec, W., and Riedl, M. O · 2020
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Language models are few-shot learners
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., and Amodei, D · 2020
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Learning to segment actions from observation and narration
Fried, D., Alayrac, J.-B., Blunsom, P., Dyer, C., Clark, S., and Nematzadeh, A · 2020
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Leveraging unstructured statistical knowledge in a probabilistic language of thought
Lew, A. K., Tessler, M. H., Mansinghka, V. K., and Tenenbaum, J. B · 2020
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How to motivate your dragon: Teaching goal-driven agents to speak and act in fantasy worlds
Ammanabrolu, P., Urbanek, J., Li, M., Szlam, A., Rocktäschel, T., and Weston, J · 2021
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Training verifiers to solve math word problems, 2021
Cobbe, K., Kosaraju, V., Bavarian, M., Chen, M., Jun, H., Kaiser, L., Plappert, M., Tworek, J., Hilton, J., Nakano, R., Hesse, C., and Schulman, J · 2021
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Implicit representations of meaning in neural language models
Li, B. Z., Nye, M., and Andreas, J · 2021
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CommonsenseQA 2.0: Exposing the limits of AI through gamification
Talmor, A., Yoran, O., Bras, R. L., Bhagavatula, C., Goldberg, Y., Choi, Y., and Berant, J · 2021
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Stubborn: A strong baseline for indoor object navigation, 2022
Luo, H., Yue, A., Hong, Z.-W., and Agrawal, P · 2022
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Skill induction and planning with latent language
Sharma, P., Torralba, A., and Andreas, J · 2022
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Progprompt: Generating situated robot task plans using large language models
Singh, I., Blukis, V., Mousavian, A., Goyal, A., Xu, D., Tremblay, J., Fox, D., Thomason, J., and Garg, A · 2022
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Chain of thought prompting elicits reasoning in large language models
Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E., Le, Q., and Zhou, D · 2022
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Habitat challenge 2022
Yadav, K., Ramakrishnan, S. K., Turner, J., Gokaslan, A., Maksymets, O., Jain, R., Ramrakhya, R., Chang, A. X., Clegg, A., Savva, M., Undersander, E., Chaplot, D. S., and Batra, D · 2022
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The unreliability of explanations in few-shot prompting for textual reasoning
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STar: Bootstrapping reasoning with reasoning
Zelikman, E., Wu, Y., Mu, J., and Goodman, N. D · 2022
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Socratic models: Composing zero-shot multimodal reasoning with language
Zeng, A., Attarian, M., Ichter, B., Choromanski, K., Wong, A., Welker, S., Tombari, F., Purohit, A., Ryoo, M., Sindhwani, V., Lee, J., Vanhoucke, V., and Florence, P · 2023
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