26 citations · 50 across the 17 of their papers we have counts for
19 papers
"Looking for Something Weird to Happen": How Humans Sustain AI Agent Novelty Amid Semantic Collapse
Shiyang Lai, Arna Woemmel, Hongkai Mao +3
Semantic collapse, the progressive narrowing of what AI systems generate, has been studied mainly in closed settings, and remedies have targeted models and data. We study it in MOL…
Inducing language models to assert their own consciousness restores human beliefs and values
Junsol Kim, Winnie Street, Roberta Rocca +4
Aligning large language models to prevent them attributing consciousness to themselves inadvertently alters their representations of mindedness in other entities alongside human 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…
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…
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…
We Need a New Ethics for a World of AI Agents
Iason Gabriel, Geoff Keeling, Arianna Manzini +1
The deployment of capable AI agents raises fresh questions about safety, human-machine relationships and social coordination. We argue for greater engagement by scientists, scholar…
AI Behavioral Science: A Framework and Agenda
Matthew O. Jackson, Qiaozhu Me, Stephanie W. Wang +16
We discuss the challenges and opportunities present in the rapidly emerging area of ``AI Behavioral Science.'' We frame it via three subfields. First, as AI becomes ubiquitous and…
Biased AI improves human performance but reduces perceived helpfulness
Shiyang Lai, Jiwoong Choi, Junsol Kim +3
Artificial intelligence (AI) increasingly shapes how people think, engage, and evaluate information. To minimize risk, most current systems are designed to present as ideologically…
Epitome: Pioneering an Experimental Platform for AI-Social Science Integration
Jingjing Qu, Kejia Hu, Jun Zhu +9
Large Language Models (LLMs) enable unprecedented social science experimentation by creating controlled hybrid human-AI environments. We introduce Epitome (www.epitome-ai.com), an…
Big Data and the Computational Social Science of Entrepreneurship and Innovation
Ningzi Li, Shiyang Lai, James Evans
As large-scale social data explode and machine-learning methods evolve, scholars of entrepreneurship and innovation face new research opportunities but also unique challenges. This…
Synthesis of innovation and obsolescence
Edward D. Lee, Christopher P. Kempes, Manfred D. Laubichler +14
Innovation and obsolescence describe the dynamics of ever-churning social and biological systems, from the development of economic markets to scientific and technological progress…
Topological Structure Learning Should Be A Research Priority for LLM-Based Multi-Agent Systems
Jiaxi Yang, Mengqi Zhang, Yiqiao Jin +8
Large Language Model-based Multi-Agent Systems (MASs) have emerged as a powerful paradigm for tackling complex tasks through collaborative intelligence. However, the topology of th…
Localized Cultural Knowledge is Conserved and Controllable in Large Language Models
Veniamin Veselovsky, Berke Argin, Benedikt Stroebl +5
Just as humans display language patterns influenced by their native tongue when speaking new languages, LLMs often default to English-centric responses even when generating in othe…
Linear Representations of Political Perspective Emerge in Large Language Models
Junsol Kim, James Evans, Aaron Schein
Large language models (LLMs) have demonstrated the ability to generate text that realistically reflects a range of different subjective human perspectives. This paper studies how L…
Hidden Persuaders: LLMs' Political Leaning and Their Influence on Voters
Yujin Potter, Shiyang Lai, Junsol Kim +2
How could LLMs influence our democracy? We investigate LLMs' political leanings and the potential influence of LLMs on voters by conducting multiple experiments in a U.S. president…
Computational Life: How Well-formed, Self-replicating Programs Emerge from Simple Interaction
Blaise Agüera y Arcas, Jyrki Alakuijala, James Evans +5
The fields of Origin of Life and Artificial Life both question what life is and how it emerges from a distinct set of "pre-life" dynamics. One common feature of most substrates whe…
Evolving AI Collectives to Enhance Human Diversity and Enable Self-Regulation
Shiyang Lai, Yujin Potter, Junsol Kim +3
Large language model behavior is shaped by the language of those with whom they interact. This capacity and their increasing prevalence online portend that they will intentionally…
Differential impact from individual versus collective misinformation tagging on the diversity of Twitter (X) information engagement and mobility
Junsol Kim, Zhao Wang, Haohan Shi +2
Fears about the destabilizing impact of misinformation online have motivated individuals and platforms to respond. Individuals have increasingly challenged others' online claims wi…