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Recent efforts have augmented large language models (LLMs) with external resources (e.g., the Internet) or internal control flows (e.g., prompt chaining) for tasks requiring grounding or reasoning, leading to a new class of language agents.
A set of postulates for the foundation of logic
A. Church · 1932
Earlier work this paper cites.
On computable numbers, with an application to the entscheidungsproblem
A. M. Turing et al · 1936
Earlier work this paper cites.
Formal reductions of the general combinatorial decision problem
E. L. Post · 1943
Earlier work this paper cites.
The theory of algorithms
A. A. Markov · 1954
Earlier work this paper cites.
Three models for the description of language
N. Chomsky · 1956
Earlier work this paper cites.
Studies in problem solving: Subject 3 on the crypt-arithmetic task DONALD+ GERALD= ROBERT
A. Newell · 1967
Earlier work this paper cites.
Human memory: A proposed system and its control processes
R. C. Atkinson and R. M. Shiffrin · 1968
Earlier work this paper cites.
Human problem solving
A. Newell and H. A. Simon · 1972
Earlier work this paper cites.
Understanding natural language
T. Winograd · 1972
Earlier work this paper cites.
Working memory
A. D. Baddeley and G. Hitch · 1974
Earlier work this paper cites.
Physical symbol systems
A. Newell · 1980
Earlier work this paper cites.
Shakey the robot
N. J. Nilsson · 1984
Earlier work this paper cites.
Chunking in Soar: The anatomy of a general learning mechanism
J. E. Laird, P. S. Rosenbloom, and A. Newell · 1986
Earlier work this paper cites.
Soar: An architecture for general intelligence
J. E. Laird, A. Newell, and P. S. Rosenbloom · 1987
Earlier work this paper cites.
Symbolic architectures for cognition
A. Newell, P. S. Rosenbloom, and J. E. Laird · 1989
Earlier work this paper cites.
Précis of unified theories of cognition
A. Newell · 1992
Earlier work this paper cites.
Intelligent agents for interactive simulation environments
M. Tambe, W. L. Johnson, R. M. Jones, F. Koss, J. E. Laird, P. S. Rosenbloom, and K. Schwamb · 1995
Earlier work this paper cites.
Intelligent tutoring goes to school in the big city
K. R. Koedinger, J. R. Anderson, W. H. Hadley, M. A. Mark, et al · 1997
Earlier work this paper cites.
Principia mathematica to* 56 , volume 2
A. N. Whitehead and B. Russell · 1997
Earlier work this paper cites.
Automated intelligent pilots for combat flight simulation
R. M. Jones, J. E. Laird, P. E. Nielsen, K. J. Coulter, P. Kenny, and F. V. Koss · 1999
Earlier work this paper cites.
Speech & language processing
D. Jurafsky · 2000
Earlier work this paper cites.
The Newell test for a theory of cognition
J. R. Anderson and C. Lebiere · 2003
Earlier work this paper cites.
Desiderata for cognitive architectures
R. Sun · 2004
Earlier work this paper cites.
Soar-RL: Integrating reinforcement learning with Soar
S. Nason and J. E. Laird · 2005
Earlier work this paper cites.
Extending cognitive architecture with episodic memory
A. M. Nuxoll and J. E. Laird · 2007
Earlier work this paper cites.
Ros: an open-source robot operating system
M. Quigley · 2009
Earlier work this paper cites.
Learning to interpret natural language navigation instructions from observations
D. Chen and R. Mooney · 2011
Earlier work this paper cites.
Understanding natural language commands for robotic navigation and mobile manipulation
S. Tellex, T. Kollar, S. Dickerson, M. Walter, A. Banerjee, S. Teller, and N. Roy · 2011
Earlier work this paper cites.
Mapping the landscape of human-level artificial general intelligence
S. Adams, I. Arel, J. Bach, R. Coop, R. Furlan, B. Goertzel, J. S. Hall, A. Samsonovich, M. Scheutz, M. Schlesinger, et al · 2012
Earlier work this paper cites.
Learning to win by reading manuals in a Monte-Carlo framework
S. Branavan, D. Silver, and R. Barzilay · 2012
Earlier work this paper cites.
A survey of Monte Carlo tree search methods
C. B. Browne, E. Powley, D. Whitehouse, S. M. Lucas, P. I. Cowling, P. Rohlfshagen, S. Tavener, D. Perez, S. Samothrakis, and S. Colton · 2012
Earlier work this paper cites.
A multi-domain evaluation of scaling in a general episodic memory
N. Derbinsky, J. Li, and J. Laird · 2012
Earlier work this paper cites.
Cognitive robotics using the Soar cognitive architecture
J. E. Laird, K. R. Kinkade, S. Mohan, and J. Z. Xu · 2012
Earlier work this paper cites.
Acquiring grounded representations of words with situated interactive instruction
S. Mohan, A. H. Mininger, J. R. Kirk, and J. E. Laird · 2012
Earlier work this paper cites.
Artificial Intelligence: A Modern Approach
S. Russell and P. Norvig · 2013
Earlier work this paper cites.
Interactive task learning for simple games
J. R. Kirk and J. E. Laird · 2014
Earlier work this paper cites.
Learning goal-oriented hierarchical tasks from situated interactive instruction
S. Mohan and J. Laird · 2014
Earlier work this paper cites.
Markov decision processes: discrete stochastic dynamic programming
M. L. Puterman · 2014
Earlier work this paper cites.
Machine Learning: The High Interest Credit Card of Technical Debt
D. Sculley, G. Holt, D. Golovin, E. Davydov, T. Phillips, D. Ebner, V. Chaudhary, and M. Young · 2014
Earlier work this paper cites.
J. Weston, S. Chopra, and A. Bordes · 2014
Earlier work this paper cites.
Practical planning: extending the classical AI planning paradigm
D. E. Wilkins · 2014
Earlier work this paper cites.
Computational rationality: A converging paradigm for intelligence in brains, minds, and machines
S. J. Gershman, E. J. Horvitz, and J. B. Tenenbaum · 2015
Earlier work this paper cites.
Towards a dataset for human computer communication via grounded language acquisition
Y. Bisk, D. Marcu, and W. Wong · 2016
Earlier work this paper cites.
C. Blundell, B. Uria, A. Pritzel, Y. Li, A. Ruderman, J. Z. Leibo, J. Rae, D. Wierstra, and D. Hassabis · 2016
Earlier work this paper cites.
Openai gym, 2016
G. Brockman, V. Cheung, L. Pettersson, J. Schneider, J. Schulman, J. Tang, and W. Zaremba · 2016
Earlier work this paper cites.
Building machines that learn and think like people, 2016
B. M. Lake, T. D. Ullman, J. B. Tenenbaum, and S. J. Gershman · 2016
Earlier work this paper cites.
Toward integrating cognitive linguistics and cognitive language processing
P. Lindes and J. E. Laird · 2016
Earlier work this paper cites.
Learning language games through interaction
S. I. Wang, P. Liang, and C. D. Manning · 2016
Earlier work this paper cites.
Reading Wikipedia to answer open-domain questions
D. Chen, A. Fisch, J. Weston, and A. Bordes · 2017
Earlier work this paper cites.
Deep reinforcement learning from human preferences
P. F. Christiano, J. Leike, T. Brown, M. Martic, S. Legg, and D. Amodei · 2017
Earlier work this paper cites.
Imitation learning: A survey of learning methods
A. Hussein, M. M. Gaber, E. Elyan, and C. Jayne · 2017
Earlier work this paper cites.
Neural episodic control
A. Pritzel, B. Uria, S. Srinivasan, A. P. Badia, O. Vinyals, D. Hassabis, D. Wierstra, and C. Blundell · 2017
Earlier work this paper cites.
Active preference-based learning of reward functions
D. Sadigh, A. D. Dragan, S. Sastry, and S. A. Seshia · 2017
Earlier work this paper cites.
World of Bits: An Open-Domain platform for web-based agents
T. Shi, A. Karpathy, L. Fan, J. Hernandez, and P. Liang · 2017
Earlier work this paper cites.
Attention is all you need
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin · 2017
Earlier work this paper cites.
Search engine guided neural machine translation
J. Gu, Y. Wang, K. Cho, and V. O. Li · 2018
Earlier work this paper cites.
G. Irving, P. Christiano, and D. Amodei · 2018
Earlier work this paper cites.
Prioritized memory access explains planning and hippocampal replay
M. G. Mattar and N. D. Daw · 2018
Earlier work this paper cites.
Deep transfer in reinforcement learning by language grounding
K. Narasimhan, R. Barzilay, and T. Jaakkola · 2018
Earlier work this paper cites.
Reinforcement learning: An introduction
R. S. Sutton and A. G. Barto · 2018
Earlier work this paper cites.
Emotional chatting machine: Emotional conversation generation with internal and external memory
H. Zhou, M. Huang, T. Zhang, X. Zhu, and B. Liu · 2018
Earlier work this paper cites.
Asking easy questions: A user-friendly approach to active reward learning
E. Biyik and M. Palan · 2019
Earlier work this paper cites.
Textworld: A learning environment for text-based games
M.-A. Côté, A. Kádár, X. Yuan, B. Kybartas, T. Barnes, E. Fine, J. Moore, M. Hausknecht, L. El Asri, M. Adada, et al · 2019
Earlier work this paper cites.
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova · 2019
Earlier work this paper cites.
Go-explore: a new approach for hard-exploration problems
A. Ecoffet, J. Huizinga, J. Lehman, K. O. Stanley, and J. Clune · 2019
Earlier work this paper cites.
Combining q-learning and search with amortized value estimates
J. B. Hamrick, V. Bapst, A. Sanchez-Gonzalez, T. Pfaff, T. Weber, L. Buesing, and P. W. Battaglia · 2019
Cited alongside, same era.
An introduction to the planning domain definition language , volume 13
P. Haslum, N. Lipovetzky, D. Magazzeni, C. Muise, R. Brachman, F. Rossi, and P. Stone · 2019
Cited alongside, same era.
The Soar cognitive architecture
J. E. Laird · 2019
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Extending machine language models toward human-level language understanding
J. L. McClelland, F. Hill, M. Rudolph, J. Baldridge, and H. Schütze · 2019
Cited alongside, same era.
Collaborative dialogue in Minecraft
A. Narayan-Chen, P. Jayannavar, and J. Hockenmaier · 2019
Cited alongside, same era.
Self-critiquing models for assisting human evaluators
W. Saunders, C. Yeh, J. Wu, S. Bills, L. Ouyang, J. Ward, and J. Leike · 2022
Later among the works it cites.
Pddl planning with pretrained large language models
T. Silver, V. Hariprasad, R. S. Shuttleworth, N. Kumar, T. Lozano-Pérez, and L. P. Kaelbling · 2022
Later among the works it cites.
How to talk so AI will learn: Instructions, descriptions, and autonomy
T. Sumers, R. Hawkins, M. K. Ho, T. Griffiths, and D. Hadfield-Menell · 2022
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Multi-stage episodic control for strategic exploration in text games
J. Tuyls, S. Yao, S. Kakade, and K. Narasimhan · 2022
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Large language models still can’t plan (a benchmark for llms on planning and reasoning about change)
K. Valmeekam, A. Olmo, S. Sreedharan, and S. Kambhampati · 2022
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K. Nguyen and H. Daumé III · 2019
Cited alongside, same era.
Vision-based navigation with language-based assistance via imitation learning with indirect intervention
K. Nguyen, D. Dey, C. Brockett, and B. Dolan · 2019
Cited alongside, same era.
Document expansion by query prediction, 2019
R. Nogueira, W. Yang, J. Lin, and K. Cho · 2019
Cited alongside, same era.
Language models are unsupervised multitask learners
A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, I. Sutskever, et al · 2019
Cited alongside, same era.
Calibration, entropy rates, and memory in language models
M. Braverman, X. Chen, S. Kakade, K. Narasimhan, C. Zhang, and Y. Zhang · 2020
Cited alongside, same era.
Language models are few-shot learners
T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, et al · 2020
Cited alongside, same era.
Making pre-trained language models better few-shot learners
T. Gao, A. Fisch, and D. Chen · 2020
Cited alongside, same era.
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AI chains: Transparent and controllable human-AI interaction by chaining large language model prompts
T. Wu, M. Terry, and C. J. Cai · 2022
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STaR: Bootstrapping reasoning with reasoning
E. Zelikman, Y. Wu, J. Mu, and N. Goodman · 2022
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Socratic models: Composing zero-shot multimodal reasoning with language
A. Zeng, M. Attarian, B. Ichter, K. Choromanski, A. Wong, S. Welker, F. Tombari, A. Purohit, M. Ryoo, V. Sindhwani, et al · 2022
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Process and content in decisions from memory
W. J. Zhao, R. Richie, and S. Bhatia · 2022
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Introducing our multimodal models, 2023
R. Bavishi, E. Elsen, C. Hawthorne, M. Nye, A. Odena, A. Somani, and S. Taşırlar · 2023
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RT-2: Vision-language-action models transfer web knowledge to robotic control
A. Brohan, N. Brown, J. Carbajal, Y. Chebotar, X. Chen, K. Choromanski, T. Ding, D. Driess, A. Dubey, C. Finn, et al · 2023
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Chateval: Towards better llm-based evaluators through multi-agent debate
C.-M. Chan, W. Chen, Y. Su, J. Yu, W. Xue, S. Zhang, J. Fu, and Z. Liu · 2023
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Open-vocabulary queryable scene representations for real world planning
B. Chen, F. Xia, B. Ichter, K. Rao, K. Gopalakrishnan, M. S. Ryoo, A. Stone, and D. Kappler · 2023
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Selection-inference: Exploiting large language models for interpretable logical reasoning
A. Creswell, M. Shanahan, and I. Higgins · 2023
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G. Dagan, F. Keller, and A. Lascarides · 2023
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Mind2Web: Towards a generalist agent for the web
X. Deng, Y. Gu, B. Zheng, S. Chen, S. Stevens, B. Wang, H. Sun, and Y. Su · 2023
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Self-collaboration code generation via chatgpt
Y. Dong, X. Jiang, Z. Jin, and G. Li · 2023
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Palm-e: An embodied multimodal language model
D. Driess, F. Xia, M. S. Sajjadi, C. Lynch, A. Chowdhery, B. Ichter, A. Wahid, J. Tompson, Q. Vuong, T. Yu, et al · 2023
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Improving factuality and reasoning in language models through multiagent debate
Y. Du, S. Li, A. Torralba, J. B. Tenenbaum, and I. Mordatch · 2023
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Releasing Persimmon-8B, 2023
E. Elsen, A. Odena, M. Nye, S. Taşırlar, T. Dao, C. Hawthorne, D. Moparthi, and A. Somani · 2023
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S. Feng, C. Y. Park, Y. Liu, and Y. Tsvetkov · 2023
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The capacity for moral self-correction in large language models
D. Ganguli, A. Askell, N. Schiefer, T. Liao, K. Lukošiūtė, A. Chen, A. Goldie, A. Mirhoseini, C. Olsson, D. Hernandez, et al · 2023
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S3: Social-network simulation system with large language model-empowered agents
C. Gao, X. Lan, Z. Lu, J. Mao, J. Piao, H. Wang, D. Jin, and Y. Li · 2023
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L. Guan, K. Valmeekam, S. Sreedharan, and S. Kambhampati · 2023
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Guidance, 2023
Guidance · 2023
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A real-world webagent with planning, long context understanding, and program synthesis
I. Gur, H. Furuta, A. Huang, M. Safdari, Y. Matsuo, D. Eck, and A. Faust · 2023
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Reasoning with language model is planning with world model
S. Hao, Y. Gu, H. Ma, J. J. Hong, Z. Wang, D. Z. Wang, and Z. Hu · 2023
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Sapien: Affective virtual agents powered by large language models
M. Hasan, C. Ozel, S. Potter, and E. Hoque · 2023
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Instruct2Act: Mapping Multi-modality Instructions to Robotic Actions with Large Language Model
S. Huang, Z. Jiang, H. Dong, Y. Qiao, P. Gao, and H. Li · 2023
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Cgmi: Configurable general multi-agent interaction framework
S. Jinxin, Z. Jiabao, W. Yilei, W. Xingjiao, L. Jiawen, and H. Liang · 2023
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Language models can solve computer tasks
G. Kim, P. Baldi, and S. McAleer · 2023
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Improving Knowledge Extraction from LLMs for Robotic Task Learning through Agent Analysis
J. R. Kirk, W. Robert, P. Lindes, and J. E. Laird · 2023
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Bridging rl theory and practice with the effective horizon
C. Laidlaw, S. Russell, and A. Dragan · 2023
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Code as policies: Language model programs for embodied control
J. Liang, W. Huang, F. Xia, P. Xu, K. Hausman, B. Ichter, P. Florence, and A. Zeng · 2023
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Swiftsage: A generative agent with fast and slow thinking for complex interactive tasks
B. Y. Lin, Y. Fu, K. Yang, P. Ammanabrolu, F. Brahman, S. Huang, C. Bhagavatula, Y. Choi, and X. Ren · 2023
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LlamaIndex, 2023
LlamaIndex · 2023
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Z. Ma, Y. Mei, and Z. Su · 2023
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Self-refine: Iterative refinement with self-feedback
A. Madaan, N. Tandon, P. Gupta, S. Hallinan, L. Gao, S. Wiegreffe, U. Alon, N. Dziri, S. Prabhumoye, Y. Yang, et al · 2023
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Augmented language models: a survey
G. Mialon, R. Dessì, M. Lomeli, C. Nalmpantis, R. Pasunuru, R. Raileanu, B. Rozière, T. Schick, J. Dwivedi-Yu, A. Celikyilmaz, et al · 2023
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Language models are bounded pragmatic speakers
K. X. Nguyen · 2023
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Lever: Learning to verify language-to-code generation with execution
A. Ni, S. Iyer, D. Radev, V. Stoyanov, W.-t. Yih, S. Wang, and X. V. Lin · 2023
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Towards a unified agent with foundation models
N. D. Palo, A. Byravan, L. Hasenclever, M. Wulfmeier, N. Heess, and M. Riedmiller · 2023
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Generative agents: Interactive simulacra of human behavior
J. S. Park, J. C. O’Brien, C. J. Cai, M. R. Morris, P. Liang, and M. S. Bernstein · 2023
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Language guided state abstractions
A. Peng, I. Sucholutsky, B. Li, T. R. Sumers, T. L. Griffiths, J. Andreas, and J. A. Shah · 2023
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Communicative agents for software development
C. Qian, X. Cong, C. Yang, W. Chen, Y. Su, J. Xu, Z. Liu, and M. Sun · 2023
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Toolllm: Facilitating large language models to master 16000+ real-world apis
Y. Qin, S. Liang, Y. Ye, K. Zhu, L. Yan, Y. Lu, Y. Lin, X. Cong, X. Tang, B. Qian, et al · 2023
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Robots that ask for help: Uncertainty alignment for large language model planners
A. Z. Ren, A. Dixit, A. Bodrova, S. Singh, S. Tu, N. Brown, P. Xu, L. Takayama, F. Xia, Z. Xu, et al · 2023
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O. J. Romero, J. Zimmerman, A. Steinfeld, and A. Tomasic · 2023
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Code llama: Open foundation models for code
B. Rozière, J. Gehring, F. Gloeckle, S. Sootla, I. Gat, X. Tan, Y. Adi, J. Liu, T. Remez, J. Rapin, A. Kozhevnikov, I. Evtimov, J. Bitton, M. P. Bhatt, C. C. Ferrer, A. Grattafiori, W. Xiong, A. D’efossez, J. Copet, F. Azhar, H. Touvron, L. Martin, N. Usunier, T. Scialom, and G. Synnaeve · 2023
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Toolformer: Language models can teach themselves to use tools
T. Schick, J. Dwivedi-Yu, R. Dessì, R. Raileanu, M. Lomeli, L. Zettlemoyer, N. Cancedda, and T. Scialom · 2023
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Reflexion: Language agents with verbal reinforcement learning
N. Shinn, F. Cassano, B. Labash, A. Gopinath, K. Narasimhan, and S. Yao · 2023
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Generalized Planning in PDDL Domains with Pretrained Large Language Models
T. Silver, S. Dan, K. Srinivas, J. B. Tenenbaum, L. P. Kaelbling, and M. Katz · 2023
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Progprompt: Generating situated robot task plans using large language models
I. Singh, V. Blukis, A. Mousavian, A. Goyal, D. Xu, J. Tremblay, D. Fox, J. Thomason, and A. Garg · 2023
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Distilling internet-scale vision-language models into embodied agents
T. Sumers, K. Marino, A. Ahuja, R. Fergus, and I. Dasgupta · 2023
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Gemini: a family of highly capable multimodal models
G. Team, R. Anil, S. Borgeaud, Y. Wu, J.-B. Alayrac, J. Yu, R. Soricut, J. Schalkwyk, A. M. Dai, A. Hauth, et al · 2023
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Llm-powered autonomous agents
L. Weng · 2023
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L. Wong, G. Grand, A. K. Lew, N. D. Goodman, V. K. Mansinghka, J. Andreas, and J. B. Tenenbaum · 2023
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Autogen: Enabling next-gen llm applications via multi-agent conversation framework
Q. Wu, G. Bansal, J. Zhang, Y. Wu, S. Zhang, E. Zhu, B. Li, L. Jiang, X. Zhang, and C. Wang · 2023
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The rise and potential of large language model based agents: A survey
Z. Xi, W. Chen, X. Guo, W. He, Y. Ding, B. Hong, M. Zhang, J. Wang, S. Jin, E. Zhou, et al · 2023
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Y. Xia, M. Shenoy, N. Jazdi, and M. Weyrich · 2023
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Olagpt: Empowering llms with human-like problem-solving abilities
Y. Xie, T. Xie, M. Lin, W. Wei, C. Li, B. Kong, L. Chen, C. Zhuo, B. Hu, and Z. Li · 2023
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Intercode: Standardizing and benchmarking interactive coding with execution feedback
J. Yang, A. Prabhakar, K. Narasimhan, and S. Yao · 2023
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Language agents in the digital world: Opportunities and risks
S. Yao and K. Narasimhan · 2023
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Tree of thoughts: Deliberate problem solving with large language models
S. Yao, D. Yu, J. Zhao, I. Shafran, T. L. Griffiths, Y. Cao, and K. Narasimhan · 2023
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