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Leveraging advanced reasoning capabilities and extensive world knowledge of large language models (LLMs) to construct generative agents for solving complex real-world problems is a major trend.
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C. H. Song, J. Wu, C. Washington, B. M. Sadler, W.-L. Chao, and Y. Su, “Llm-planner: Few-shot grounded planning for embodied agents with large language models,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 2998–3009
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2023
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A. Zhao, D. Huang, Q. Xu, M. Lin, Y.-J. Liu, and G. Huang, “Expel: Llm agents are experiential learners,” 2023
2023
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M. Shanahan, K. McDonell, and L. Reynolds, “Role play with large language models,” Nature , vol. 623, no. 7987, pp. 493–498, 2023
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C. Cui, Y. Ma, X. Cao, W. Ye, and Z. Wang, “Drive as you speak: Enabling human-like interaction with large language models in autonomous vehicles,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision , 2024, pp. 902–909
2024
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D. Fu, X. Li, L. Wen, M. Dou, P. Cai, B. Shi, and Y. Qiao, “Drive like a human: Rethinking autonomous driving with large language models,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision , 2024, pp. 910–919
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