MindEvolve is introduced, an autonomous workflow designed to predict behavior in social interactions by generating interpretable symbolic models of cognition, and provides a roadmap for advancing LLM-based cognitive modeling toward human-expert-level theory construction.
Different capacities for mentalization across LLMs are demonstrated, and cognitive computational modeling is highlighted as a formal method for assessing comparative intelligence across humans and machines.
Aamir Sohail, Xintong Zhong, Arkady Konovalov et al.· 0 citations
Large language models (LLMs) now power the reasoning core of intelligent virtual agents deployed across an expanding range of social settings, from tutoring students and supporting patients in healthcare, to mediating group discussions and representing humans in various social settings. Effective deployment demands soc...
Kevin Kurian, Kevin Scroggins, Emmanuel Dorley et al.· Proceedings of the 26th ACM...· 0 citations
Large language models (LLMs) increasingly shape communication, learning, work, creativity, and decision-making, yet social-science research on these developments remains fragmented. We map this emerging field using a curated corpus of 198 papers reviewed in full and a field-scale corpus of 47,719 published papers from...
Statistical reasoning is multidimensional, yet evaluations of large language models (LLMs) typically emphasize response accuracy while overlooking how models construct and communicate statistical explanations. This study demonstrates the value of a multidimensional evaluation by combining response accuracy, response be...
Recent public and/or intellectual controversies around large language models (LLMs) forcefully demonstrate the relevance of language to social life. In both computer science and computational social science (CSS) research, their development has led to a surge of interest and engagement with the complexity of natural la...
Language models are appealing tools for research on the past. But to trust the evidence a model provides, researchers need to know whether its responses fit the period represented. Validation is challenging, because this is not a task living people ordinarily perform, and because many questions have multiple correct an...
Ted Underwood, Zi-Liang Qiu, Sarah Griebel et al.· 0 citations
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