The posts collectively reflect interest and support for the research, with examples like references to the False Belief Task and phrases such as 'comparing human and GPT-3 performance' suggest deep engagement. Overall, the tone and content emphasize curiosity and acknowledgment of the study's importance in advancing understanding of AI and human cognition.
AgreementModerate agreement
Most discussions show support for the idea that large language models like GPT-3 can differentiate mental states, although some acknowledge limitations.
InterestModerate interest
Posts express curiosity and enthusiasm about the research, especially highlighted by comments on the significance of the findings.
EngagementModerate engagement
Participants reference specific experiments like the False Belief Task and discuss the implications, demonstrating active engagement.
ImpactModerate impact
The discussions emphasize the potential influence of this research on AI understanding and cognitive science, indicating moderate impact.
Abstract in brief
The study explores whether large language models like GPT-3 understand human beliefs by testing them with a linguistic version of the False Belief Task, comparing their responses to those of humans.
Results show that GPT-3 can differentiate between different mental states and perform above chance, but humans still outperform the model, indicating that language exposure alone isn't enough to fully explain humans' ability to understand others' beliefs.
This suggests that humans likely develop their understanding of mental states through multiple mechanisms, not just exposure to language, highlighting the role of innate or other experiential factors beyond statistical learning from language data.]
Really excited that our paper comparing human and GPT-3 performance at the False Belief Task has been published in Cognitive Science https://t.co/L0PowtiMBS @cogsci_soc @Sean_Trott @jamichaelov @tylerachang
Does GPT-3 have a theory of mind?
Check out the blog published in CogSci Blogs based on this paper:
https://t.co/79HXlCKVSl https://t.co/kWeXlzLEpY
Oct 3, 2023 · 33 likes · 8 shares · 2,718 followersView post →
AM
Akira Murakami
@mrkm_a · X
"while statistical learning from language exposure may in part explain how humans develop the ability to reason about the mental states of others, other mechanisms are also responsible."
Trott et al. (2023). Do Large Language Models Know What Humans Know?
https://t.co/5h8FNTRImk
This study investigates whether large language models like GPT-3 comprehend human beliefs by testing them with a linguistic false belief task. Results show models differentiate mental states but still lag behind human…
Artificial intelligences like Chat GPT and Gemini generate responses based on statistical probabilities rather than genuine understanding or awareness. They do not possess true intelligence or reasoning capabilities…
Reading List for the Interested:
https://t.co/o572chfhuD
https://t.co/D58GFGCJOm
https://t.co/f57yvMAHTG
https://t.co/oytipstWr3
https://t.co/maH2nNdkRT
https://t.co/iUkRMpOYdb
https://t.co/xOPpGQ5yhl
https://t.co/0EZBtfBCLk
https://t.co/SUHWDy3K9m
https://t.co/aPbcxjgiBw
Feb 6, 2025 · 1 like · 1 share · 11,698 followersView post →
@Starcourse @binarybits @JohnRentoul @tylercowen Agree, and we make a similar point here (https://t.co/L0PowtiMBS) and here (https://t.co/Euc3FaFLPP). Other great work testing the robustness of LLM ToM!https://t.co/ojzH0Wpi6j @NatalieShapira @MaartenSap ; https://t.co/UmlHiRapT7 @gandhikanishk @jphilippfranken @tobigerstenberg
"while statistical learning from language exposure may in part explain how humans develop the ability to reason about the mental states of others, other mechanisms are also responsible."
Trott et al. (2023). Do Large Language Models Know What Humans Know?
https://t.co/5h8FNTRImk
Really excited that our paper comparing human and GPT-3 performance at the False Belief Task has been published in Cognitive Science https://t.co/L0PowtiMBS @cogsci_soc @Sean_Trott @jamichaelov @tylerachang
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