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Theory of Mind (ToM), the ability to understand people's mental states, is an essential ingredient for developing machines with human-level social intelligence.
Socialiqa: Commonsense reasoning about social interactions
Maarten Sap, Hannah Rashkin, Derek Chen, Ronan LeBras, and Yejin Choi. 2019 · 1904
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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 · 1983
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Planning and acting in partially observable stochastic domains
Leslie Pack Kaelbling, Michael L Littman, and Anthony R Cassandra. 1998 · 1998
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Socially intelligent robots: dimensions of human–robot interaction
Kerstin Dautenhahn. 2007 · 2007
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Action understanding as inverse planning
Chris L Baker, Rebecca Saxe, and Joshua B Tenenbaum. 2009 · 2009
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Watch-and-help: A challenge for social perception and human-ai collaboration
Xavier Puig, Tianmin Shu, Shuang Li, Zilin Wang, Yuan-Hong Liao, Joshua B Tenenbaum, Sanja Fidler, and Antonio Torralba. 2020 · 2010
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Stylepredict: Machine theory of mind for human driver behavior from trajectories
Rohan Chandra, Aniket Bera, and Dinesh Manocha. 2020 · 2011
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The happiness of the fish: Evidence for a common theory of one’s own and others’ actions
Rebecca Saxe. 2012 · 2012
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Vqa: Visual question answering
Stanislaw Antol, Aishwarya Agrawal, Jiasen Lu, Margaret Mitchell, Dhruv Batra, C Lawrence Zitnick, and Devi Parikh. 2015 · 2015
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Commonsense interpretation of triangle behavior
Andrew S. Gordon. 2016 · 2016
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Cooperative inverse reinforcement learning
Dylan Hadfield-Menell, Stuart J Russell, Pieter Abbeel, and Anca Dragan. 2016 · 2016
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The naïve utility calculus: Computational principles underlying commonsense psychology
Julian Jara-Ettinger, Hyowon Gweon, Laura E Schulz, and Joshua B Tenenbaum. 2016 · 2016
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Rational quantitative attribution of beliefs, desires and percepts in human mentalizing
Chris L Baker, Julian Jara-Ettinger, Rebecca Saxe, and Joshua B Tenenbaum. 2017 · 2017
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Building machines that learn and think like people
Brenden M Lake, Tomer D Ullman, Joshua B Tenenbaum, and Samuel J Gershman. 2017 · 2017
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Evaluating theory of mind in question answering
Aida Nematzadeh, Kaylee Burns, Erin Grant, Alison Gopnik, and Thomas L Griffiths. 2018 · 2018
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Machine theory of mind
Neil Rabinowitz, Frank Perbet, Francis Song, Chiyuan Zhang, SM Ali Eslami, and Matthew Botvinick. 2018 · 2018
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Theory of mind as inverse reinforcement learning
Julian Jara-Ettinger. 2019 · 2019
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Revisiting the evaluation of theory of mind through question answering
Matt Le, Y-Lan Boureau, and Maximilian Nickel. 2019 · 2019
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Social-iq: A question answering benchmark for artificial social intelligence
Amir Zadeh, Michael Chan, Paul Pu Liang, Edmund Tong, and Louis-Philippe Morency. 2019 · 2019
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From recognition to cognition: Visual commonsense reasoning
Rowan Zellers, Yonatan Bisk, Ali Farhadi, and Yejin Choi. 2019 · 2019
Cited alongside, same era.
Baby intuitions benchmark (bib): Discerning the goals, preferences, and actions of others
Kanishk Gandhi, Gala Stojnic, Brenden M Lake, and Moira R Dillon. 2021 · 2021
Cited alongside, same era.
Exploring roberta’s theory of mind through textual entailment
John Hewitt and Michael Cohen. 2021 · 2021
Cited alongside, same era.
Lora: Low-rank adaptation of large language models
Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. 2021 · 2021
Cited alongside, same era.
Phase: Physically-grounded abstract social events for machine social perception
Aviv Netanyahu, Tianmin Shu, Boris Katz, Andrei Barbu, and Joshua B Tenenbaum. 2021 · 2021
Cited alongside, same era.
Instructblip: Towards general-purpose vision-language models with instruction tuning
Wenliang Dai, Junnan Li, Dongxu Li, Anthony Meng Huat Tiong, Junqi Zhao, Weisheng Wang, Boyang Li, Pascale Fung, and Steven Hoi. 2023 · 2023
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Mme: A comprehensive evaluation benchmark for multimodal large language models
Chaoyou Fu, Peixian Chen, Yunhang Shen, Yulei Qin, Mengdan Zhang, Xu Lin, Zhenyu Qiu, Wei Lin, Jinrui Yang, Xiawu Zheng, et al. 2023 · 2023
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Understanding social reasoning in language models with language models
Kanishk Gandhi, Jan-Philipp Fränken, Tobias Gerstenberg, and Noah D Goodman. 2023 · 2023
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Hi-tom: A benchmark for evaluating higher-order theory of mind reasoning in large language models
Yinghui He, Yufan Wu, Yilin Jia, Rada Mihalcea, Yulong Chen, and Naihao Deng. 2023 · 2023
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Agent: A benchmark for core psychological reasoning
Tianmin Shu, Abhishek Bhandwaldar, Chuang Gan, Kevin Smith, Shari Liu, Dan Gutfreund, Elizabeth Spelke, Joshua Tenenbaum, and Tomer Ullman. 2021 · 2021
Cited alongside, same era.
Mimoqa: Multimodal input multimodal output question answering
Hrituraj Singh, Anshul Nasery, Denil Mehta, Aishwarya Agarwal, Jatin Lamba, and Balaji Vasan Srinivasan. 2021 · 2021
Cited alongside, same era.
Multimodalqa: Complex question answering over text, tables and images
Alon Talmor, Ori Yoran, Amnon Catav, Dan Lahav, Yizhong Wang, Akari Asai, Gabriel Ilharco, Hannaneh Hajishirzi, and Jonathan Berant. 2021 · 2021
Cited alongside, same era.
GPT-J-6B: A 6 Billion Parameter Autoregressive Language Model
Ben Wang and Aran Komatsuzaki. 2021 · 2021
Cited alongside, same era.
Towards mutual theory of mind in human-ai interaction: How language reflects what students perceive about a virtual teaching assistant
Qiaosi Wang, Koustuv Saha, Eric Gregori, David Joyner, and Ashok Goel. 2021 · 2021
Cited alongside, same era.
A persistent spatial semantic representation for high-level natural language instruction execution
Valts Blukis, Chris Paxton, Dieter Fox, Animesh Garg, and Yoav Artzi. 2022 · 2022
Cited alongside, same era.
Language models as zero-shot planners: Extracting actionable knowledge for embodied agents
Wenlong Huang, Pieter Abbeel, Deepak Pathak, and Igor Mordatch. 2022 · 2022
Cited alongside, same era.
Fantom: A benchmark for stress-testing machine theory of mind in interactions
Hyunwoo Kim, Melanie Sclar, Xuhui Zhou, Ronan Le Bras, Gunhee Kim, Yejin Choi, and Maarten Sap. 2023 · 2023
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Theory of mind may have spontaneously emerged in large language models
Michal Kosinski. 2023 · 2023
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M 3 it: A large-scale dataset towards multi-modal multilingual instruction tuning
Lei Li, Yuwei Yin, Shicheng Li, Liang Chen, Peiyi Wang, Shuhuai Ren, Mukai Li, Yazheng Yang, Jingjing Xu, Xu Sun, et al. 2023 · 2023
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Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. 2023 · 2023
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Boosting theory-of-mind performance in large language models via prompting
Shima Rahimi Moghaddam and Christopher J Honey. 2023 · 2023
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OpenAI. 2023 · 2023
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Xavier Puig, Tianmin Shu, Joshua B Tenenbaum, and Antonio Torralba. 2023 · 2023
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Multivent: Multilingual videos of events with aligned natural text
Kate Sanders, David Etter, Reno Kriz, and Benjamin Van Durme. 2023 · 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 · 2023
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Clever hans or neural theory of mind? stress testing social reasoning in large language models
Natalie Shapira, Mosh Levy, Seyed Hossein Alavi, Xuhui Zhou, Yejin Choi, Yoav Goldberg, Maarten Sap, and Vered Shwartz. 2023 · 2023
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Llama 2: Open foundation and fine-tuned chat models
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al. 2023 · 2023
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Large language models fail on trivial alterations to theory-of-mind tasks
Tomer Ullman. 2023 · 2023
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Think twice: Perspective-taking improves large language models’ theory-of-mind capabilities
Alex Wilf, Sihyun Shawn Lee, Paul Pu Liang, and Louis-Philippe Morency. 2023 · 2023
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Video-llama: An instruction-tuned audio-visual language model for video understanding
Hang Zhang, Xin Li, and Lidong Bing. 2023 · 2023
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