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Language models (LMs) can solve tasks such as answering questions about tables or images by writing programs.
Online learning of relaxed ccg grammars for parsing to logical form
Zettlemoyer, L. and Collins, M · 2007
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Learning minimal abstractions
Liang, P., Tripp, O., and Naik, M · 2011
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Weakly Supervised Learning of Semantic Parsers for Mapping Instructions to Actions
Artzi, Y. and Zettlemoyer, L · 2013
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Compositional semantic parsing on semi-structured tables
Pasupat, P. and Liang, P · 2015
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A syntactic neural model for general-purpose code generation
Yin, P. and Neubig, G · 2017
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Gqa: A new dataset for real-world visual reasoning and compositional question answering
Hudson, D. A. and Manning, C. D · 2019
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Program synthesis and semantic parsing with learned code idioms, 2019
Shin, R., Allamanis, M., Brockschmidt, M., and Polozov, O · 2019
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Measuring mathematical problem solving with the math dataset
Hendrycks, D., Burns, C., Kadavath, S., Arora, A., Basart, S., Tang, E., Song, D., and Steinhardt, J · 2021
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Leveraging language to learn program abstractions and search heuristics
Wong, C., Ellis, K. M., Tenenbaum, J., and Andreas, J · 2021
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Chen, W., Ma, X., Wang, X., and Cohen, W. W · 2022
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HiTab: A hierarchical table dataset for question answering and natural language generation
Cheng, Z., Dong, H., Wang, Z., Jia, R., Guo, J., Gao, Y., Han, S., Lou, J.-G., and Zhang, D · 2022
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Visual programming: Compositional visual reasoning without training
Gupta, T. and Kembhavi, A · 2022
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Natural language to code translation with execution
Shi, F., Fried, D., Ghazvininejad, M., Zettlemoyer, L., and Wang, S. I · 2022
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Top-down synthesis for library learning
Bowers, M., Olausson, T. X., Wong, L., Grand, G., Tenenbaum, J. B., Ellis, K., and Solar-Lezama, A · 2023
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Large language models as tool makers
Cai, T., Wang, X., Ma, T., Chen, X., and Zhou, D · 2023
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Dynamic prompt learning via policy gradient for semi-structured mathematical reasoning
Lu, P., Qiu, L., Chang, K.-W., Wu, Y. N., Zhu, S.-C., Rajpurohit, T., Clark, P., and Kalyan, A · 2023
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Clin: A continually learning language agent for rapid task adaptation and generalization
Majumder, B. P., Mishra, B. D., Jansen, P., Tafjord, O., Tandon, N., Zhang, L., Callison-Burch, C., and Clark, P · 2023
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Lever: Learning to verify language-to-code generation with execution
Ni, A., Iyer, S., Radev, D., Stoyanov, V., Yih, W.-t., Wang, S., and Lin, X. V · 2023
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Qian, C., Han, C., Fung, Y. R., Qin, Y., Liu, Z., and Ji, H · 2023
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Modular visual question answering via code generation
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Cao, Y., Chen, S., Liu, R., Wang, Z., and Fried, D · 2023
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Binding language models in symbolic languages
Cheng, Z., Xie, T., Shi, P., Li, C., Nadkarni, R., Hu, Y., Xiong, C., Radev, D., Ostendorf, M., Zettlemoyer, L., Smith, N. A., and Yu, T · 2023
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Dreamcoder: growing generalizable, interpretable knowledge with wake–sleep bayesian program learning
Ellis, K., Wong, L., Nye, M., Sable-Meyer, M., Cary, L., Anaya Pozo, L., Hewitt, L., Solar-Lezama, A., and Tenenbaum, J. B · 2023
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Lilo: Learning interpretable libraries by compressing and documenting code
Grand, G., Wong, L., Bowers, M., Olausson, T. X., Liu, M., Tenenbaum, J. B., and Andreas, J · 2023
Cited alongside, same era.
Assistgpt: A general multi-modal assistant that can plan, execute, inspect, and learn
Gao, D., Ji, L., Zhou, L., Lin, K. Q., Chen, J., Fan, Z., and Shou, M. Z
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Pal: Program-aided language models
Gao, L., Madaan, A., Zhou, S., Alon, U., Liu, P., Yang, Y., Callan, J., and Neubig, G
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Competition-level code generation with alphacode
Li, Y., Choi, D., Chung, J., Kushman, N., Schrittwieser, J., Leblond, R., Eccles, T., Keeling, J., Gimeno, F., Dal Lago, A., et al
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Subramanian, S., Narasimhan, M., Khangaonkar, K., Yang, K., Nagrani, A., Schmid, C., Zeng, A., Darrell, T., and Klein, D · 2023
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Vipergpt: Visual inference via python execution for reasoning
Surís, D., Menon, S., and Vondrick, C · 2023
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Lego-prover: Neural theorem proving with growing libraries
Xin, H., Wang, H., Zheng, C., Li, L., Liu, Z., Cao, Q., Huang, Y., Xiong, J., Shi, H., Xie, E., et al · 2023
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Mm-react: Prompting chatgpt for multimodal reasoning and action
Yang, Z., Li, L., Wang, J., Lin, K., Azarnasab, E., Ahmed, F., Liu, Z., Liu, C., Zeng, M., and Wang, L · 2023
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Craft: Customizing llms by creating and retrieving from specialized toolsets
Yuan, L., Chen, Y., Wang, X., Fung, Y. R., Peng, H., and Ji, H · 2023
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