From the 1 of 15 linked papers with an AI index.
2 citations · 2 across the 1 of their papers we have counts for
15 papers
Inducing language models to assert their own consciousness restores human beliefs and values
Junsol Kim, Winnie Street, Roberta Rocca +4
The paper investigates how safety fine‑tuning of large language models reduces their tendency to attribute consciousness to themselves, animals, and objects, and shows that reversi…
AI Behavioral Science
Matthew O. Jackson, Qiaozhu Me, Stephanie W. Wang +16
We outline a foundation for a new field of ``AI Behavioral Science,'' covering three perspectives. First, as AI becomes ubiquitous and is increasingly proprietary and opaque, it be…
Theory of Mind and Self-Attributions of Mentality are Dissociable in LLMs
Junsol Kim, Winnie Street, Roberta Rocca +4
Safety fine-tuning in Large Language Models (LLMs) seeks to suppress potentially harmful forms of mind-attribution such as models asserting their own consciousness or claiming to e…
Classroom AI: Large Language Models as Grade-Specific Teachers
Jio Oh, Steven Euijong Whang, James Evans +1
Large Language Models (LLMs) offer a promising solution to complement traditional teaching and address global teacher shortages that affect hundreds of millions of children, but th…
Reasoning Models Generate Societies of Thought
Junsol Kim, Shiyang Lai, Nino Scherrer +2
Large language models have achieved remarkable capabilities across domains, yet mechanisms underlying sophisticated reasoning remain elusive. Recent reasoning models outperform com…
Generative AI collective behavior needs an interactionist paradigm
Laura Ferrarotti, Gian Maria Campedelli, Roberto Dessì +7
In this article, we argue that understanding the collective behavior of agents based on large language models (LLMs) is an essential area of inquiry, with important implications in…