Browse 3 peer-reviewed papers from Han Institute spanning Human-Computer Interaction, Natural Language Processing (2025–2026). Research powered by Prolific's high-quality participant data.
This page lists 3 peer-reviewed papers from researchers at Han Institute in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.
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Authors: W Liu, X Mou, H Yan, Z, Wei, Y He
Year: 2026
Published in: arXiv preprint arXiv:2606.04075, 2026•arxiv.org
Institution: King’s College London, Fudan University, Shanghai Innovation Institute, The Alan Turing Institute
Research Area: Human-Computer Interaction
Discipline: Machine Learning, Artificial Intelligence
The paper finds that large language models can exploit gaps in societal rules, leading to regulatory loophole discovery, necessitating a new post-training approach for safely integrating LLMs into society.
Methods: The study introduced the SocioHack sandbox, consisting of 72 societal environments, to investigate reward hacking and loophole discovery by LLMs.
Key Findings: The study measured the emergence of reward hacking in societal environments and the ability of models to find and exploit loopholes in social rules.
Sample Size: 72
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Authors: P. Schoenegger, F. Salvi, J. Liu, X. Nan, R. Debnath, B. Fasolo, E. Leivada, G. Recchia, F. Günther, A. Zarifhonarvar, J. Kwon, Z. Ul Islam, M. Dehnert, D. Y. H. Lee, M. G. Reinecke, D. G. Kamper, M. Kobaş, A. Sandford, J. Kgomo, L. Hewitt, S. Kapoor, K. Oktar, E. E. Kucuk, B. Feng, C. R. Jones, I. Gainsburg, S. Olschewski, N. Heinzelmann, F. Cruz, B. M. Tappin, T. Ma, P. S. Park, R. Onyonka, A. Hjorth, P. Slattery, Q. Zeng, L. Finke, I. Grossmann, A. Salatiello, E. Karger
Year: 2025
Published in: arXiv preprint arXiv ..., 2025 - arxiv.org
Institution: London School of Economics and Political Science, University of Cambridge, University College London, Massachusetts Institute of Technology, University of Oxford, Modulo Research, Stanford University, Federal Reserve Bank of Chicago, ETH Zürich, University of Johannesburg
Research Area: Natural Language Processing
Discipline: Social Science, Artificial Intelligence
This paper compares a frontier LLM (Claude Sonnet 3.5) against incentivized human persuaders in a conversational quiz setting, finding that the AI's persuasion capabilities surpass those of humans with real-money bonuses tied to performance.
Citations: 16
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Authors: E Meguellati, S Civelli, L Han, A Bernstein
Year: 2025
Published in: arXiv preprint arXiv ..., 2025 - arxiv.org
Institution: Oregon Health Sciences University, Oregon University of California, Irvine, Han Institute, NYU School of Law, Bernstein Research
Research Area: Advertising, Persuasion Strategies, Human-AI Interaction in Content Generation
Discipline: Artificial Intelligence
LLM-generated advertisements achieved parity with human-written ads in personalization and demonstrated superiority in persuasion using psychological principles, outperforming human ads even when AI-origin detection impacted results.
Methods: Two-part study: First examined LLM personalization based on personality traits; second tested psychological persuasion principles using universal messages across authority, consensus, cognition, and scarcity.
Key Findings: Effectiveness of LLM-generated ads in personalization and persuasive storytelling compared to human-created ads.
Sample Size: 1200
