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Performing tasks on the web presents fundamental challenges to large language models (LLMs), including combinatorially large open-world tasks and variations across web interfaces.
Formal languages and their relation to automata
John E Hopcroft and Jeffrey D Ullman · 1969
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Between mdps and semi-mdps: Learning, planning, and representing knowledge at multiple temporal scales
Richard S Sutton · 1998
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Algorithms for deterministic incremental dependency parsing
Joakim Nivre · 2008
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Reinforcement learning for mapping instructions to actions
Satchuthananthavale RK Branavan, Harr Chen, Luke S Zettlemoyer, and Regina Barzilay · 2009
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Weakly supervised learning of semantic parsers for mapping instructions to actions
Yoav Artzi and Luke Zettlemoyer · 2013
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World of bits: An open-domain platform for web-based agents
Tianlin Shi, Andrej Karpathy, Linxi Fan, Jonathan Hernandez, and Percy Liang · 2017
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A deep hierarchical approach to lifelong learning in minecraft
Chen Tessler, Shahar Givony, Tom Zahavy, Daniel Mankowitz, and Shie Mannor · 2017
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Feudal networks for hierarchical reinforcement learning
Alexander Sasha Vezhnevets, Simon Osindero, Tom Schaul, Nicolas Heess, Max Jaderberg, David Silver, and Koray Kavukcuoglu · 2017
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Reinforcement learning on web interfaces using workflow-guided exploration
Evan Zheran Liu, Kelvin Guu, Panupong Pasupat, Tianlin Shi, and Percy Liang · 2018
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Data-efficient hierarchical reinforcement learning
Ofir Nachum, Shixiang Shane Gu, Honglak Lee, and Sergey Levine · 2018
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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Hierarchical control of situated agents through natural language
Shuyan Zhou, Pengcheng Yin, and Graham Neubig · 2021
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A data-driven approach for learning to control computers
Peter C Humphreys, David Raposo, Tobias Pohlen, Gregory Thornton, Rachita Chhaparia, Alistair Muldal, Josh Abramson, Petko Georgiev, Adam Santoro, and Timothy Lillicrap · 2022
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Code as policies: Language model programs for embodied control
Jacky Liang, Wenlong Huang, Fei Xia, Peng Xu, Karol Hausman, Brian Ichter, Pete Florence, and Andy Zeng · 2022
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Training language models to follow instructions with human feedback
Long Ouyang, Jeff Wu, Xu Jiang, Diogo Almeida, Carroll L Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al · 2022
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Adapt: As-needed decomposition and planning with language models
Archiki Prasad, Alexander Koller, Mareike Hartmann, Peter Clark, Ashish Sabharwal, Mohit Bansal, and Tushar Khot · 2023
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Toolformer: Language models can teach themselves to use tools
Timo Schick, Jane Dwivedi-Yu, Roberto Dessì, Roberta Raileanu, Maria Lomeli, Luke Zettlemoyer, Nicola Cancedda, and Thomas Scialom · 2023
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Reflexion: Language agents with verbal reinforcement learning.(2023)
Noah Shinn, Federico Cassano, Beck Labash, Ashwin Gopinath, Karthik Narasimhan, and Shunyu Yao · 2023
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Llm-planner: Few-shot grounded planning for embodied agents with large language models
Chan Hee Song, Jiaman Wu, Clayton Washington, Brian M Sadler, Wei-Lun Chao, and Yu Su · 2023
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WebArena: A realistic web environment for building autonomous agents
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Syeda Nahida Akter, Zichun Yu, Aashiq Muhamed, Tianyue Ou, Alex Bäuerle, Ángel Alexander Cabrera, Krish Dholakia, Chenyan Xiong, and Graham Neubig · 2023
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Mind2web: Towards a generalist agent for the web
Xiang Deng, Yu Gu, Boyuan Zheng, Shijie Chen, Samuel Stevens, Boshi Wang, Huan Sun, and Yu Su · 2023
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Decomposed prompting: A modular approach for solving complex tasks
Tushar Khot, Harsh Trivedi, Matthew Finlayson, Yao Fu, Kyle Richardson, Peter Clark, and Ashish Sabharwal · 2023
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Language models can solve computer tasks
Geunwoo Kim, Pierre Baldi, and Stephen McAleer · 2023
Cited alongside, same era.
Lost in the middle: How language models use long contexts. arxiv 2023
Nelson F Liu, Kevin Lin, John Hewitt, Ashwin Paranjape, Michele Bevilacqua, Fabio Petroni, and Percy Liang · 2023
Cited alongside, same era.
Laser: Llm agent with state-space exploration for web navigation
Kaixin Ma, Hongming Zhang, Hongwei Wang, Xiaoman Pan, and Dong Yu · 2023
Cited alongside, same era.
GPT-4 technical report, 2023
OpenAI · 2023
Cited alongside, same era.
Environment generation for zero-shot compositional reinforcement learning
Izzeddin Gur, Natasha Jaques, Yang Miao, Jiayuan Choi, Manik Tiwari, Honglak Lee, and Alex Faust
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Shuyan Zhou, Frank F Xu, Hao Zhu, Xuhui Zhou, Robert Lo, Abishek Sridhar, Xianyi Cheng, Yonatan Bisk, Daniel Fried, Uri Alon, et al · 2023
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Multimodal web navigation with instruction-finetuned foundation models
Hiroki Furuta, Ofir Nachum, Kuang-Huei Lee, Yutaka Matsuo, Shixiang Shane Gu, and Izzeddin Gur · 2024
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Rap: Retrieval-augmented planning with contextual memory for multimodal llm agents
Tomoyuki Kagaya, Thong Jing Yuan, Yuxuan Lou, Jayashree Karlekar, Sugiri Pranata, Akira Kinose, Koki Oguri, Felix Wick, and Yang You · 2024
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Adaplanner: Adaptive planning from feedback with language models
Haotian Sun, Yuchen Zhuang, Lingkai Kong, Bo Dai, and Chao Zhang · 2024
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Gpt4tools: Teaching large language model to use tools via self-instruction
Rui Yang, Lin Song, Yanwei Li, Sijie Zhao, Yixiao Ge, Xiu Li, and Ying Shan · 2024
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Synapse: Trajectory-as-exemplar prompting with memory for computer control
Longtao Zheng, Rundong Wang, Xinrun Wang, and Bo An · 2024
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