activity
20242026
most citedReasoning Models Generate Societies of Thought

2 citations · 7 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CL2026

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…

cs.CL20262 cited

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…

cs.HC20252 cited

Biased AI improves human decision-making but reduces trust

Shiyang Lai, Junsol Kim, Nadav Kunievsky +2

Current AI systems minimize risk by enforcing ideological neutrality, yet this may introduce automation bias by suppressing cognitive engagement in human decision-making. We conduc…

cs.CL20251 cited

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…

cs.CL20242 cited

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…