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"Thinking is for Doing." Humans can infer other people's mental states from observations--an ability called Theory-of-Mind (ToM)--and subsequently act pragmatically on those inferences.
A formal basis for the heuristic determination of minimum cost paths
Peter E Hart, Nils J Nilsson, and Bertram Raphael · 1968
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Does the chimpanzee have a theory of mind?
David Premack and Guy Woodruff · 1978
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Beliefs about beliefs: Representation and constraining function of wrong beliefs in young children’s understanding of deception
Heinz Wimmer and Josef Perner · 1983
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Does the autistic child have a “theory of mind”?
Simon Baron-Cohen, Alan M Leslie, and Uta Frith · 1985
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Three-year-olds’ difficulty with false belief: The case for a conceptual deficit
Josef Perner, Susan R Leekam, and Heinz Wimmer · 1987
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Thinking is for doing: portraits of social cognition from daguerreotype to laserphoto
Susan T Fiske · 1992
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The consideration of future consequences: Weighing immediate and distant outcomes of behavior
Alan Strathman, Faith Gleicher, David S Boninger, and C Scott Edwards · 1994
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Recognition of faux pas by normally developing children and children with asperger syndrome or high-functioning autism
Simon Baron-Cohen, Michelle O’riordan, Valerie Stone, Rosie Jones, and Kate Plaisted · 1999
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Development and neurophysiology of mentalizing
Uta Frith and Christopher D Frith · 2003
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Evaluating theory of mind in question answering
Aida Nematzadeh, Kaylee Burns, Erin Grant, Alison Gopnik, and Tom Griffiths · 2018
Earlier work this paper cites.
Revisiting the evaluation of theory of mind through question answering
Matthew Le, Y-Lan Boureau, and Maximilian Nickel · 2019
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Social IQa: Commonsense reasoning about social interactions
Maarten Sap, Hannah Rashkin, Derek Chen, Ronan Le Bras, and Yejin Choi · 2019
Earlier work this paper cites.
Language models are few-shot learners
Tom B Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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Mindcraft: Theory of mind modeling for situated dialogue in collaborative tasks
Cristian-Paul Bara, CH-Wang Sky, and Joyce Chai · 2021
Cited alongside, same era.
Reframing Instructional Prompts to GPTk’s Language
Swaroop Mishra, Daniel Khashabi, Chitta Baral, Yejin Choi, and Hannaneh Hajishirzi · 2021
Cited alongside, same era.
Language models as agent models
Jacob Andreas · 2022
Cited alongside, same era.
Large language models are zero-shot reasoners
Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa · 2022
Cited alongside, same era.
Chatgpt: Optimizing language models for dialogue, 2022
OpenAI · 2022
Cited alongside, same era.
Measuring and narrowing the compositionality gap in language models
A real-world webagent with planning, long context understanding, and program synthesis
Izzeddin Gur, Hiroki Furuta, Austin Huang, Mustafa Safdari, Yutaka Matsuo, Douglas Eck, and Aleksandra Faust · 2023
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Reasoning with language model is planning with world model
Shibo Hao, Yi Gu, Haodi Ma, Joshua Jiahua Hong, Zhen Wang, Daisy Zhe Wang, and Zhiting Hu · 2023
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Theory of mind may have spontaneously emerged in large language models
Michal Kosinski · 2023
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Dissociating language and thought in large language models: a cognitive perspective
Kyle Mahowald, Anna A Ivanova, Idan A Blank, Nancy Kanwisher, Joshua B Tenenbaum, and Evelina Fedorenko · 2023
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Gpt-4 technical report
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Ofir Press, Muru Zhang, Sewon Min, Ludwig Schmidt, Noah A Smith, and Mike Lewis · 2022
Cited alongside, same era.
Neural theory-of-mind? on the limits of social intelligence in large LMs
Maarten Sap, Ronan Le Bras, Daniel Fried, and Yejin Choi · 2022
Cited alongside, same era.
Interactive query-assisted summarization via deep reinforcement learning
Ori Shapira, Ramakanth Pasunuru, Mohit Bansal, Ido Dagan, and Yael Amsterdamer · 2022
Cited alongside, same era.
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
Cited alongside, same era.
Least-to-Most Prompting Enables Complex Reasoning in Large Language Models
Denny Zhou, Nathanael Schärli, Le Hou, Jason Wei, Nathan Scales, Xuezhi Wang, Dale Schuurmans, Olivier Bousquet, Quoc Le, and Ed Chi · 2022
Cited alongside, same era.
Rohan Anil, Andrew M Dai, Orhan Firat, Melvin Johnson, Dmitry Lepikhin, Alexandre Passos, Siamak Shakeri, Emanuel Taropa, Paige Bailey, Zhifeng Chen, et al · 2023
Cited alongside, same era.
Graph of thoughts: Solving elaborate problems with large language models
Maciej Besta, Nils Blach, Ales Kubicek, Robert Gerstenberger, Lukas Gianinazzi, Joanna Gajda, Tomasz Lehmann, Michal Podstawski, Hubert Niewiadomski, Piotr Nyczyk, et al · 2023
Cited alongside, same era.
R OpenAI · 2023
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Generative agents: Interactive simulacra of human behavior
Joon Sung Park, Joseph C O’Brien, Carrie J Cai, Meredith Ringel Morris, Percy Liang, and Michael S Bernstein · 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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Minding language models’(lack of) theory of mind: A plug-and-play multi-character belief tracker
Melanie Sclar, Sachin Kumar, Peter West, Alane Suhr, Yejin Choi, and Yulia Tsvetkov · 2023
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How well do large language models perform on faux pas tests?
Natalie Shapira, Guy Zwirn, and Yoav Goldberg · 2023
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Do large language models know what humans know?
Sean Trott, Cameron Jones, Tyler Chang, James Michaelov, and Benjamin Bergen · 2023
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Large language models fail on trivial alterations to theory-of-mind tasks
Tomer Ullman · 2023
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I cast detect thoughts: Learning to converse and guide with intents and theory-of-mind in dungeons and dragons
Pei Zhou, Andrew Zhu, Jennifer Hu, Jay Pujara, Xiang Ren, Chris Callison-Burch, Yejin Choi, and Prithviraj Ammanabrolu · 2023
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