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There emerges a promising trend of using large language models (LLMs) to generate code-like plans for complex inference tasks such as visual reasoning.
The empirical case for two systems of reasoning
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Clevr: A diagnostic dataset for compositional language and elementary visual reasoning
Johnson, J., Hariharan, B., Van Der Maaten, L., Fei-Fei, L., Lawrence Zitnick, C., and Girshick, R · 2017
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Ptr: A benchmark for part-based conceptual, relational, and physical reasoning
Hong, Y., Yi, L., Tenenbaum, J., Torralba, A., and Gan, C · 2021
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Towards reasoning in large language models: A survey
Huang, J. and Chang, K. C.-C · 2022
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Training language models to follow instructions with human feedback
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al · 2022
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Llm-planner: Few-shot grounded planning for embodied agents with large language models
Song, C. H., Wu, J., Washington, C., Sadler, B. M., Chao, W.-L., and Su, Y · 2022
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Chain-of-thought prompting elicits reasoning in large language models
Wei, J., Wang, X., Schuurmans, D., Bosma, M., Xia, F., Chi, E., Le, Q. V., Zhou, D., et al · 2022
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React: Synergizing reasoning and acting in language models
Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., and Cao, Y · 2022
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Bang, Y., Cahyawijaya, S., Lee, N., Dai, W., Su, D., Wilie, B., Lovenia, H., Ji, Z., Yu, T., Chung, W., et al · 2023
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Bubeck, S., Chandrasekaran, V., Eldan, R., Gehrke, J., Horvitz, E., Kamar, E., Lee, P., Lee, Y. T., Li, Y., Lundberg, S., et al · 2023
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Gupta, T. and Kembhavi, A · 2023
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Jiang, X., Dong, Y., Wang, L., Shang, Q., and Li, G · 2023
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Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis
Toolformer: Language models can teach themselves to use tools
Schick, T., Dwivedi-Yu, J., Dessì, R., Raileanu, R., Lomeli, M., Zettlemoyer, L., Cancedda, N., and Scialom, T · 2023
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Shen, Y., Song, K., Tan, X., Li, D., Lu, W., and Zhuang, Y · 2023
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Shinn, N., Labash, B., and Gopinath, A · 2023
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Chatgpt for robotics: Design principles and model abilities. microsoft, 2023
Vemprala, S., Bonatti, R., Bucker, A., and Kapoor, A · 2023
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Wolfram plugin for chatgpt
Wolfram · 2023
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Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al · 2023
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Large language model guided tree-of-thought
Long, J · 2023
Cited alongside, same era.
Augmented language models: a survey
Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al · 2023
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Toolllm: Facilitating large language models to master 16000+ real-world apis
Qin, Y., Liang, S., Ye, Y., Zhu, K., Yan, L., Lu, Y., Lin, Y., Cong, X., Tang, X., Qian, B., et al · 2023
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Wu, C., Yin, S., Qi, W., Wang, X., Tang, Z., and Duan, N · 2023
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Translating natural language to planning goals with large-language models
Xie, Y., Yu, C., Zhu, T., Bai, J., Gong, Z., and Soh, H · 2023
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Tree of thoughts: Deliberate problem solving with large language models
Yao, S., Yu, D., Zhao, J., Shafran, I., Griffiths, T. L., Cao, Y., and Narasimhan, K · 2023
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