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Large Language Models (LLMs) have exhibited remarkable performance on various Natural Language Processing (NLP) tasks.
Multi-hop reading comprehension across multiple documents by reasoning over heterogeneous graphs
Tu, M., Wang, G., Huang, J., Tang, Y., He, X., Zhou, B., 2019 · 1905
Earlier work this paper cites.
PIQA: reasoning about physical commonsense in natural language
Bisk, Y., Zellers, R., Bras, R.L., Gao, J., Choi, Y., 2019 · 1911
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Approaches to abductive reasoning: an overview
Paul, G., 1993 · 1993
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Beyond inductive and deductive reasoning: The search for a sense of knowing
Simon, M.A., 1996 · 1996
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Properties of inductive reasoning
Heit, E., 2000 · 2000
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The evolution of mathematical reasoning: Everyday versus idealized understandings
Schliemann, A.D., Carraher, D.W., 2002 · 2002
Earlier work this paper cites.
Designing effective supports for causal reasoning
Jonassen, D., Ionas, I., 2006 · 2006
Earlier work this paper cites.
Mindreaders: the cognitive basis of” theory of mind”
Apperly, I., 2010 · 2010
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Inductive reasoning
Hayes, B.K., Heit, E., Swendsen, H., 2010 · 2010
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Deductive reasoning
Johnson-Laird, P., 2010 · 2010
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Mathematical reasoning: Analogies, metaphors, and images
English, L.D., 2013 · 2013
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Abductive reasoning
Walton, D., 2014 · 2014
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Towards ai-complete question answering: A set of prerequisite toy tasks
Weston, J., Bordes, A., Chopra, S., Mikolov, T., 2016 · 2016
Earlier work this paper cites.
Deep reinforcement learning from human preferences
Christiano, P.F., Leike, J., Brown, T., Martic, M., Legg, S., Amodei, D., 2017 · 2017
Earlier work this paper cites.
Modeling semantic plausibility by injecting world knowledge
Wang, S., Durrett, G., Erk, K., 2018 · 2018
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HotpotQA: A dataset for diverse, explainable multi-hop question answering
Yang, Z., Qi, P., Zhang, S., Bengio, Y., Cohen, W., Salakhutdinov, R., Manning, C.D., 2018 · 2018
Earlier work this paper cites.
Analysing mathematical reasoning abilities of neural models
Saxton, D., Grefenstette, E., Hill, F., Kohli, P., 2019 · 2019
Earlier work this paper cites.
CLUTRR: A diagnostic benchmark for inductive reasoning from text
Sinha, K., Sodhani, S., Dong, J., Pineau, J., Hamilton, W.L., 2019 · 2019
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CommonsenseQA: A question answering challenge targeting commonsense knowledge
Talmor, A., Herzig, J., Lourie, N., Berant, J., 2019 · 2019
Earlier work this paper cites.
Abductive commonsense reasoning
Bhagavatula, C., Bras, R.L., Malaviya, C., Sakaguchi, K., Holtzman, A., Rashkin, H., Downey, D., Yih, W., Choi, Y., 2020 · 2020
Cited alongside, same era.
Language models are few-shot learners, in: Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M., Lin, H. (Eds.), Advances in Neural Information Processing Systems, Curran Associates, Inc.. pp. 1877–1901
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., Amodei, D., 2020 · 2020
Cited alongside, same era.
Improving the teaching of hypothesis testing using a divide-and-conquer strategy and content exposure control in a gamified environment
Delgado-Gómez, D., González-Landero, F., Montes-Botella, C., Sujar, A., Bayona, S., Martino, L., 2020 · 2020
Cited alongside, same era.
Commonsense reasoning for natural language processing, in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts, Association for Computational Linguistics, Online. pp. 27–33
Introducing chatgpt
OpenIA, 2022 · 2022
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Training language models to follow instructions with human feedback
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C.L., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., Schulman, J., Hilton, J., Kelton, F., Miller, L.E., Simens, M., Askell, A., Welinder, P., Christiano, P.F., Leike, J., Lowe, R.J., 2022 · 2022
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Lamda: Language models for dialog applications
Thoppilan, R., Freitas, D., Hall, J., Shazeer, N., Kulshreshtha, A., Cheng, H.T., Jin, A., Bos, T., Baker, L., Du, Y., Li, Y., Lee, H., Zheng, H., Ghafouri, A., Menegali, M., Huang, Y., Krikun, M., Lepikhin, D., Qin, J., Le, Q., 2022 · 2022
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Large language models still can’t plan (a benchmark for llms on planning and reasoning about change)
Valmeekam, K., Olmo, A., Sreedharan, S., Kambhampati, S., 2022 · 2022
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STar: Bootstrapping reasoning with reasoning
Zelikman, E., Wu, Y., Mu, J., Goodman, N., 2022 · 2022
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Sap, M., Shwartz, V., Bosselut, A., Choi, Y., Roth, D., 2020 · 2020
Cited alongside, same era.
Evaluating large language models trained on code
Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al., 2021 · 2021
Cited alongside, same era.
Training verifiers to solve math word problems
Cobbe, K., Kosaraju, V., Bavarian, M., Chen, M., Jun, H., Kaiser, L., Plappert, M., Tworek, J., Hilton, J., Nakano, R., et al., 2021 · 2021
Cited alongside, same era.
Explaining answers with entailment trees
Dalvi, B., Jansen, P., Tafjord, O., Xie, Z., Smith, H., Pipatanangkura, L., Clark, P., 2021 · 2021
Cited alongside, same era.
Scaling Language Models: Methods, Analysis & Insights from Training Gopher
Rae, J.W., Borgeaud, S., Cai, T., Millican, K., Hoffmann, J., Song, F., Aslanides, J., Henderson, S., Ring, R., Young, S., Rutherford, E., Hennigan, T., Menick, J., Cassirer, A., Powell, R., van den Driessche, G., Hendricks, L.A., Rauh, M., Huang, P.S., Glaese, A., Welbl, J., Dathathri, S., Huang, S., Uesato, J., Mellor, J., Higgins, I., Creswell, A., McAleese, N., Wu, A., Elsen, E., Jayakumar, S., Buchatskaya, E., Budden, D., Sutherland, E., Simonyan, K., Paganini, M., Sifre, L., Martens, L., Li, X.L., Kuncoro, A., Nematzadeh, A., Gribovskaya, E., Donato, D., Lazaridou, A., Mensch, A., Lespiau, J.B., Tsimpoukelli, M., Grigorev, N., Fritz, D., Sottiaux, T., Pajarskas, M., Pohlen, T., Gong, Z., Toyama, D., de Masson d’Autume, C., Li, Y., Terzi, T., Mikulik, V., Babuschkin, I., Clark, A., de Las Casas, D., Guy, A., Jones, C., Bradbury, J., Johnson, M., Hechtman, B., Weidinger, L., Gabriel, I., Isaac, W., Lockhart, E., Osindero, S., Rimell, L., Dyer, C., Vinyals, O., Ayoub, K., Stanway, J., Bennett, L., Hassabis, D., Kavukcuoglu, K., Irving, G., 2021 · 2021
Cited alongside, same era.
Palm: Scaling language modeling with pathways
Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., Schuh, P., Shi, K., Tsvyashchenko, S., Maynez, J., Rao, A., Barnes, P., Tay, Y., Shazeer, N.M., Prabhakaran, V., Reif, E., Du, N., Hutchinson, B.C., Pope, R., Bradbury, J., Austin, J., Isard, M., Gur-Ari, G., Yin, P., Duke, T., Levskaya, A., Ghemawat, S., Dev, S., Michalewski, H., García, X., Misra, V., Robinson, K., Fedus, L., Zhou, D., Ippolito, D., Luan, D., Lim, H., Zoph, B., Spiridonov, A., Sepassi, R., Dohan, D., Agrawal, S., Omernick, M., Dai, A.M., Pillai, T.S., Pellat, M., Lewkowycz, A., Moreira, E., Child, R., Polozov, O., Lee, K., Zhou, Z., Wang, X., Saeta, B., Díaz, M., Firat, O., Catasta, M., Wei, J., Meier-Hellstern, K.S., Eck, D., Dean, J., Petrov, S., Fiedel, N., 2022 · 2022
Cited alongside, same era.
Scaling instruction-finetuned language models
Chung, H.W., Hou, L., Longpre, S., Zoph, B., Tay, Y., Fedus, W., Li, E., Wang, X., Dehghani, M., Brahma, S., et al., 2022 · 2022
Cited alongside, same era.
e-CARE: a new dataset for exploring explainable causal reasoning
Du, L., Ding, X., Xiong, K., Liu, T., Qin, B., 2022 · 2022
Cited alongside, same era.
Complexity-based prompting for multi-step reasoning
Fu, Y., Peng, H.C., Sabharwal, A., Clark, P., Khot, T., 2022 · 2022
Cited alongside, same era.
Later among the works it cites.
Bang, Y., Cahyawijaya, S., Lee, N., Dai, W., Su, D., Wilie, B., Lovenia, H., Ji, Z., , Yu, T., Chung, W., Do, Q.V., Xu, Y., Fung, P., 2023 · 2023
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Sparks of artificial general intelligence: Early experiments with gpt-4
Bubeck, S., Chandrasekaran, V., Eldan, R., Gehrke, J.A., Horvitz, E., Kamar, E., Lee, P., Lee, Y.T., Li, Y.F., Lundberg, S.M., Nori, H., Palangi, H., Ribeiro, M.T., Zhang, Y., 2023 · 2023
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Mathematics, word problems, common sense, and artificial intelligence
Davis, E., 2023 · 2023
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Palm-e: An embodied multimodal language model
Driess, D., Xia, F., Sajjadi, M.S.M., Lynch, C., Chowdhery, A., Ichter, B., Wahid, A., Tompson, J., Vuong, Q., Yu, T., Huang, W., Chebotar, Y., Sermanet, P., Duckworth, D., Levine, S., Vanhoucke, V., Hausman, K., Toussaint, M., Greff, K., Zeng, A., Mordatch, I., Florence, P., 2023 · 2023
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How close is chatgpt to human experts? comparison corpus, evaluation, and detection
Guo, B., Zhang, X., Wang, Z., Jiang, M., Nie, J., Ding, Y., Yue, J., Wu, Y., 2023 · 2023
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Decomposed prompting: A modular approach for solving complex tasks
Khot, T., Trivedi, H., Finlayson, M., Fu, Y., Richardson, K., Clark, P., Sabharwal, A., 2023 · 2023
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Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing
Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., Neubig, G., 2023 · 2023
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Dissociating language and thought in large language models: a cognitive perspective
Mahowald, K., Ivanova, A.A., Blank, I.A., Kanwisher, N., Tenenbaum, J.B., Fedorenko, E., 2023 · 2023
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An overview of bard: an early experiment with generative ai
Manyika, J., 2023 · 2023
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Llama: Open and efficient foundation language models
Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al., 2023 · 2023
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Self-consistency improves chain of thought reasoning in language models
Wang, X., Wei, J., Schuurmans, D., Le, Q.V., Chi, E.H., Narang, S., Chowdhery, A., Zhou, D., 2023 · 2023
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Automatic chain of thought prompting in large language models
Zhang, Z., Zhang, A., Li, M., Smola, A., 2023 · 2023
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Teaching algorithmic reasoning via in-context learning
Zhou, H., Nova, A., Courville, A., Larochelle, H., Neyshabur, B., Sedghi, H., 2023 · 2023
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