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Recent research shows that Large Language Models (LLMs) exhibit a compelling level of proficiency in Theory of Mind (ToM) tasks.
“Language models are few-shot learners”
Tom Brown et al · 1901
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In Communications of the ACM 21.2
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“The breadth of Shamir’s secret-sharing scheme”
Ed Dawson and Diane Donovan · 1994
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“A neural substrate of prediction and reward”
Wolfram Schultz, Peter Dayan and P Montague · 1997
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“The Extended Mind”
Andy Clark and David. Chalmers · 1998
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“Do 15-month-old infants understand false beliefs?”
Kristine Onishi and Renée Baillargeon · 2005
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“Guide to attribute based access control (abac) definition and considerations (draft)”
Vincent Hu et al · 2013
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“Theory of mind: a neural prediction problem”
Jorie Koster-Hale and Rebecca Saxe · 2013
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“Theory of the firm: Managerial behavior, agency costs and ownership structure”
Michael Jensen and William Meckling · 2019
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“Confidential computing: Hardware-based trusted execution for applications and data”
Confidential Consortium · 2020
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“Prompt programming for large language models: Beyond the few-shot paradigm”
Laria Reynolds and Kyle McDonell · 2021
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“Simulators”
janus · 2022
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“Chain-of-thought prompting elicits reasoning in large language models”
Jason Wei et al · 2022
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“Theory of Mind May Have Spontaneously Emerged in Large Language Models”, 2023
Michal Kosinski · 2023
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“Are Emergent Abilities in Large Language Models just In-Context Learning?”, 2023
Sheng Lu et al · 2023
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“Boosting Theory-of-Mind Performance in Large Language Models via Prompting”, 2023
Shima Moghaddam and Christopher. Honey · 2023
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“Brain-Inspired Computational Intelligence via Predictive Coding”, 2023
Tommaso Salvatori et al · 2023
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Jason Wei et al · 2022
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Zhengbao Jiang et al · 2023
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Aojun Zhou et al · 2023
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