35 citations · 52 across the 12 of their papers we have counts for
12 papers
Representational Similarity and Model Behavior in Multi-Agent Interaction
Yujin Potter, Seun Eisape, Shiyang Lai +6
Researchers have shown that neural similarity among humans predicts social closeness and cooperative success, whereas innovation often emerges from interactions among dissimilar in…
Multi-LLM Systems Exhibit Robust Semantic Collapse
Weiyi Kong, Shiyang Lai, Jinghua Piao +1
Whether machines can originate novel content has been debated for nearly two centuries, from Lovelace's assertion that no engine can "originate anything" to Turing's question of wh…
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…
Probing Multimodal Large Language Models on Cognitive Biases in Chinese Short-Video Misinformation
Jen-tse Huang, Chang Chen, Shiyang Lai +3
Short-video platforms have become major channels for misinformation, where deceptive claims frequently leverage visual experiments and social cues. While Multimodal Large Language…
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…
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…
Signal in the Noise: Polysemantic Interference Transfers and Predicts Cross-Model Influence
Bofan Gong, Shiyang Lai, James Evans +1
Polysemanticity is pervasive in language models and remains a major challenge for interpretation and model behavioral control. Leveraging sparse autoencoders (SAEs), we map the pol…
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…
Can Editing LLMs Inject Harm?
Canyu Chen, Baixiang Huang, Zekun Li +12
Large Language Models (LLMs) have emerged as a new information channel. Meanwhile, one critical but under-explored question is: Is it possible to bypass the safety alignment and in…
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…
Can Large Language Model Agents Simulate Human Trust Behavior?
Chengxing Xie, Canyu Chen, Feiran Jia +11
Large Language Model (LLM) agents have been increasingly adopted as simulation tools to model humans in social science and role-playing applications. However, one fundamental quest…
Multidimensional Service Quality Scoring System
Shiyang Lai
This supplementary paper aims to introduce the Multidimensional Service Quality Scoring System (MSQs), a review-based method for quantifying host service quality mentioned and empl…