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Language agents show potential in being capable of utilizing natural language for varied and intricate tasks in diverse environments, particularly when built upon large language models (LLMs).
An architecture for intelligent reactive systems
Leslie Pack Kaelbling et al · 1987
Earlier work this paper cites.
Designing autonomous agents: Theory and practice from biology to engineering and back
Pattie Maes · 1990
Earlier work this paper cites.
Intelligence without representation
Rodney A Brooks · 1991
Earlier work this paper cites.
Intelligent agents: Theory and practice
Michael Wooldridge and Nicholas R Jennings · 1995
Earlier work this paper cites.
Context-free languages and pushdown automata
Jean-Michel Autebert, Jean Berstel, and Luc Boasson · 1997
Earlier work this paper cites.
Architectural styles and the design of network-based software architectures
Roy Thomas Fielding · 2000
Earlier work this paper cites.
Reinforcement learning: An introduction
Richard S. Sutton and Andrew G. Barto · 2005
Earlier work this paper cites.
Artificial intelligence a modern approach
Stuart J Russell · 2010
Earlier work this paper cites.
Redis in action
Josiah Carlson · 2013
Earlier work this paper cites.
Playing atari with deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin Riedmiller · 2013
Earlier work this paper cites.
MongoDB in action: covers MongoDB version 3.0
Kyle Banker, Douglas Garrett, Peter Bakkum, and Shaun Verch · 2016
Earlier work this paper cites.
Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
Earlier work this paper cites.
World of bits: An open-domain platform for web-based agents
Tianlin Shi, Andrej Karpathy, Linxi Fan, Jonathan Hernandez, and Percy Liang · 2017
Earlier work this paper cites.
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
Earlier work this paper cites.
Evaluating large language models trained on code
Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, et al · 2021
Earlier work this paper cites.
The personalization paradox: The conflict between accurate user models and personalized adaptive systems
Santiago Ontanon and Jichen Zhu · 2021
Earlier work this paper cites.
Adapting user interfaces with model-based reinforcement learning
Kashyap Todi, Gilles Bailly, Luis Leiva, and Antti Oulasvirta · 2021
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Langchain: Building applications with llms through composability, 2022
Harrison Chase · 2022
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Palm: Scaling language modeling with pathways
Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, et al · 2022
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Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al · 2022
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Tianbao Xie, Chen Henry Wu, Peng Shi, Ruiqi Zhong, Torsten Scholak, Michihiro Yasunaga, Chien-Sheng Wu, Ming Zhong, Pengcheng Yin, Sida I Wang, et al · 2022
Swiftsage: A generative agent with fast and slow thinking for complex interactive tasks
Bill Yuchen Lin, Yicheng Fu, Karina Yang, Prithviraj Ammanabrolu, Faeze Brahman, Shiyu Huang, Chandra Bhagavatula, Yejin Choi, and Xiang Ren · 2023
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AgentBench: Evaluating LLMs as Agents, August 2023
Xiao Liu, Hao Yu, Hanchen Zhang, Yifan Xu, Xuanyu Lei, Hanyu Lai, Yu Gu, Hangliang Ding, Kaiwen Men, Kejuan Yang, Shudan Zhang, Xiang Deng, Aohan Zeng, Zhengxiao Du, Chenhui Zhang, Sheng Shen, Tianjun Zhang, Yu Su, Huan Sun, Minlie Huang, Yuxiao Dong, and Jie Tang · 2023
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open-interpreter: Openai’s code interpreter in your terminal, running locally
Killian Lucas · 2023
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On the design of ai-powered code assistants for notebooks
Andrew M McNutt, Chenglong Wang, Robert A Deline, and Steven M Drucker · 2023
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Babyagi, 2023
Yohei Nakajima · 2023
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Spellburst: A node-based interface for exploratory creative coding with natural language prompts
Tyler Angert, Miroslav Ivan Suzara, Jenny Han, Christopher Lawrence Pondoc, and Hariharan Subramonyam · 2023
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Introducing claude, 2023
Anthropic · 2023
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Large language models as tool makers
Tianle Cai, Xuezhi Wang, Tengyu Ma, Xinyun Chen, and Denny Zhou · 2023
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Agentverse: Facilitating multi-agent collaboration and exploring emergent behaviors in agents
Weize Chen, Yusheng Su, Jingwei Zuo, Cheng Yang, Chenfei Yuan, Chen Qian, Chi-Min Chan, Yujia Qin, Yaxi Lu, Ruobing Xie, et al · 2023
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Binding language models in symbolic languages
Zhoujun Cheng, Tianbao Xie, Peng Shi, Chengzu Li, Rahul Nadkarni, Yushi Hu, Caiming Xiong, Dragomir Radev, Mari Ostendorf, Luke Zettlemoyer, Noah A. Smith, and Tao Yu · 2023
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Mind2web: Towards a generalist agent for the web, 2023
Xiang Deng, Yu Gu, Boyuan Zheng, Shijie Chen, Samuel Stevens, Boshi Wang, Huan Sun, and Yu Su · 2023
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Tora: A tool-integrated reasoning agent for mathematical problem solving, 2023
Zhibin Gou, Zhihong Shao, Yeyun Gong, yelong shen, Yujiu Yang, Minlie Huang, Nan Duan, and Weizhu Chen · 2023
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Generative Agents: Interactive Simulacra of Human Behavior, August 2023
Joon Sung Park, Joseph C. O’Brien, Carrie J. Cai, Meredith Ringel Morris, Percy Liang, and Michael S. Bernstein · 2023
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Gorilla: Large language model connected with massive apis
Shishir G Patil, Tianjun Zhang, Xin Wang, and Joseph E Gonzalez · 2023
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Cheng Qian, Chi Han, Yi R Fung, Yujia Qin, Zhiyuan Liu, and Heng Ji · 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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One embedder, any task: Instruction-finetuned text embeddings, 2023
Hongjin Su, Weijia Shi, Jungo Kasai, Yizhong Wang, Yushi Hu, Mari Ostendorf, Wen tau Yih, Noah A. Smith, Luke Zettlemoyer, and Tao Yu · 2023
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Llama 2: Open Foundation and Fine-Tuned Chat Models, July 2023
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Focused transformer: Contrastive training for context scaling
Szymon Tworkowski, Konrad Staniszewski, Mikołaj Pacek, Yuhuai Wu, Henryk Michalewski, and Piotr Miłoś · 2023
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Autogen: Enabling next-gen llm applications via multi-agent conversation framework
Qingyun Wu, Gagan Bansal, Jieyu Zhang, Yiran Wu, Shaokun Zhang, Erkang Zhu, Beibin Li, Li Jiang, Xiaoyun Zhang, and Chi Wang · 2023
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Effective long-context scaling of foundation models, 2023
Wenhan Xiong, Jingyu Liu, Igor Molybog, Hejia Zhang, Prajjwal Bhargava, Rui Hou, Louis Martin, Rashi Rungta, Karthik Abinav Sankararaman, Barlas Oguz, Madian Khabsa, Han Fang, Yashar Mehdad, Sharan Narang, Kshitiz Malik, Angela Fan, Shruti Bhosale, Sergey Edunov, Mike Lewis, Sinong Wang, and Hao Ma · 2023
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