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Understanding and attributing mental states, known as Theory of Mind (ToM), emerges as a fundamental capability for human social reasoning.
Clever Hans:(the horse of Mr. Von Osten.) a contribution to experimental animal and human psychology
Pfungst, O · 1911
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Does the autistic child have a “theory of mind”?
Baron-Cohen, S., Leslie, A. M., and Frith, U · 1985
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Pretense and representation: The origins of” theory of mind.”
Leslie, A. M · 1987
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Meta-analysis of theory-of-mind development: The truth about false belief
Wellman, H. M., Cross, D., and Watson, J · 2001
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Automatically constructing a corpus of sentential paraphrases
Dolan, B. and Brockett, C · 2005
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Do 15-month-old infants understand false beliefs?
Onishi, K. H. and Baillargeon, R · 2005
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Understanding and sharing intentions: The origins of cultural cognition
Tomasello, M., Carpenter, M., Call, J., Behne, T., and Moll, H · 2005
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The neural basis of mentalizing
Frith, C. D. and Frith, U · 2006
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Core knowledge
Spelke, E. S. and Kinzler, K. D · 2007
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Action understanding as inverse planning
Baker, C. L., Saxe, R., and Tenenbaum, J. B · 2009
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Cause and intent: Social reasoning in causal learning
Goodman, N. D., Baker, C. L., and Tenenbaum, J. B · 2009
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Functional activity of the right temporo-parietal junction and of the medial prefrontal cortex associated with true and false belief reasoning
Döhnel, K., Schuwerk, T., Meinhardt, J., Sodian, B., Hajak, G., and Sommer, M · 2012
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Linguistic regularities in continuous space word representations
Mikolov, T., Yih, W.-t., and Zweig, G · 2013
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Understanding intermediate layers using linear classifier probes
Alain, G. and Bengio, Y · 2016
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The secret of our success: How culture is driving human evolution, domesticating our species, and making us smarter
Henrich, J · 2016
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Coordinate to cooperate or compete: abstract goals and joint intentions in social interaction
Kleiman-Weiner, M., Ho, M. K., Austerweil, J. L., Littman, M. L., and Tenenbaum, J. B · 2016
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Understanding the minds of others: A neuroimaging meta-analysis
Molenberghs, P., Johnson, H., Henry, J. D., and Mattingley, J. B · 2016
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Rational quantitative attribution of beliefs, desires and percepts in human mentalizing
Baker, C. L., Jara-Ettinger, J., Saxe, R., and Tenenbaum, J. B · 2017
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Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
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Evaluating theory of mind in question answering
Nematzadeh, A., Burns, K., Grant, E., Gopnik, A., and Griffiths, T. L · 2018
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Machine theory of mind
Rabinowitz, N., Perbet, F., Song, F., Zhang, C., Eslami, S. A., and Botvinick, M · 2018
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Satisficing models of bayesian theory of mind for explaining behavior of differently uncertain agents
Track, S. I. A., Pöppel, J., and Kopp, S · 2018
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Glue: A multi-task benchmark and analysis platform for natural language understanding
Wang, A., Singh, A., Michael, J., Hill, F., Levy, O., and Bowman, S. R · 2018
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When choosing plausible alternatives, clever hans can be clever
Kavumba, P., Inoue, N., Heinzerling, B., Singh, K., Reisert, P., and Inui, K · 2019
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Revisiting the evaluation of theory of mind through question answering
Le, M., Boureau, Y.-L., and Nickel, M · 2019
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Socialiqa: Commonsense reasoning about social interactions
Sap, M., Rashkin, H., Chen, D., LeBras, R., and Choi, Y · 2019
Sparks of artificial general intelligence: Early experiments with gpt-4
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Understanding social reasoning in language models with language models
Gandhi, K., Fränken, J.-P., Gerstenberg, T., and Goodman, N · 2023
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Ai alignment: A comprehensive survey
Ji, J., Qiu, T., Chen, B., Zhang, B., Lou, H., Wang, K., Duan, Y., He, Z., Zhou, J., Zhang, Z., et al · 2023
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Jiang, A. Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D. S., Casas, D. d. l., Bressand, F., Lengyel, G., Lample, G., Saulnier, L., et al · 2023
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Theory of mind may have spontaneously emerged in large language models
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Theory of minds: Understanding behavior in groups through inverse planning
Shum, M., Kleiman-Weiner, M., Littman, M. L., and Tenenbaum, J. B · 2019
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Neural network acceptability judgments
Warstadt, A., Singh, A., and Bowman, S. R · 2019
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Understanding the role of individual units in a deep neural network
Bau, D., Zhu, J.-Y., Strobelt, H., Lapedriza, A., Zhou, B., and Torralba, A · 2020
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Measuring massive multitask language understanding
Hendrycks, D., Burns, C., Basart, S., Zou, A., Mazeika, M., Song, D., and Steinhardt, J · 2020
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The naive utility calculus as a unified, quantitative framework for action understanding
Jara-Ettinger, J., Schulz, L. E., and Tenenbaum, J. B · 2020
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On the dangers of stochastic parrots: Can language models be too big?
Bender, E. M., Gebru, T., McMillan-Major, A., and Shmitchell, S · 2021
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A mathematical framework for transformer circuits
Elhage, N., Nanda, N., Olsson, C., Henighan, T., Joseph, N., Mann, B., Askell, A., Bai, Y., Chen, A., Conerly, T., et al · 2021
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Kosinski, M · 2023
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Tomchallenges: A principle-guided dataset and diverse evaluation tasks for exploring theory of mind
Ma, X., Gao, L., and Xu, Q · 2023
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Boosting theory-of-mind performance in large language models via prompting
Moghaddam, S. R. and Honey, C. J · 2023
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The alignment problem from a deep learning perspective, 2023
Ngo, R., Chan, L., and Mindermann, S · 2023
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The linear representation hypothesis and the geometry of large language models
Park, K., Choe, Y. J., and Veitch, V · 2023
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Clever hans or neural theory of mind? stress testing social reasoning in large language models
Shapira, N., Levy, M., Alavi, S. H., Zhou, X., Choi, Y., Goldberg, Y., Sap, M., and Shwartz, V · 2023
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Function vectors in large language models
Todd, E., Li, M. L., Sharma, A. S., Mueller, A., Wallace, B. C., and Bau, D · 2023
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Large language models fail on trivial alterations to theory-of-mind tasks
Ullman, T · 2023
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Decodingtrust: A comprehensive assessment of trustworthiness in gpt models
Wang, B., Chen, W., Pei, H., Xie, C., Kang, M., Zhang, C., Xu, C., Xiong, Z., Dutta, R., Schaeffer, R., et al · 2023
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Think twice: Perspective-taking improves large language models’ theory-of-mind capabilities
Wilf, A., Lee, S. S., Liang, P. P., and Morency, L.-P · 2023
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How far are large language models from agents with theory-of-mind?
Zhou, P., Madaan, A., Potharaju, S. P., Gupta, A., McKee, K. R., Holtzman, A., Pujara, J., Ren, X., Mishra, S., Nematzadeh, A., et al · 2023
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Social motion prediction with cognitive hierarchies
Zhu, W., Qin, J., Lou, Y., Ye, H., Ma, X., Ci, H., and Wang, Y · 2023
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Deepseek llm: Scaling open-source language models with longtermism
Bi, X., Chen, D., Chen, G., Chen, S., Dai, D., Deng, C., Ding, H., Dong, K., Du, Q., Fu, Z., et al · 2024
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Language models represent space and time
Gurnee, W. and Tegmark, M · 2024
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Jiang, A. Q., Sablayrolles, A., Roux, A., Mensch, A., Savary, B., Bamford, C., Chaplot, D. S., Casas, D. d. l., Hanna, E. B., Bressand, F., et al · 2024
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Mmtom-qa: Multimodal theory of mind question answering, 2024
Jin, C., Wu, Y., Cao, J., Xiang, J., Kuo, Y.-L., Hu, Z., Ullman, T., Torralba, A., Tenenbaum, J. B., and Shu, T · 2024
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Theory of mind abilities of large language models in human-robot interaction: An illusion?
Verma, M., Bhambri, S., and Kambhampati, S · 2024
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