arXiv:2610.03998v2 Announce Type: replace
Abstract: Large language models (LLMs) are increasingly used as synthetic personas representing survey respondents. Their validity as substitutes for particu...
By Khashayar Pourtaheri, Ahmad Zareei
arXiv:2604. 03904v2 Announce Type: replace-cross Abstract: Large language models (LLMs) often produce confident but incorrect answers, in part because standard evaluation incentives reward guessing over expressing uncertainty.
By Haotian Zong, Binze Li, Yufei Long, Sinyin Chang, Jialong Wu, Gillian K. Hadfield
arXiv:2411. 10109v3 Announce Type: replace Abstract: Machine learning can predict human behavior well when substantial structured data are available for well-defined outcomes.
By Joon Sung Park, Carolyn Q. Zou, Jonne Kamphorst, Niles Egan, Aaron Shaw, Benjamin Mako Hill, Carrie Cai, Meredith Ringel Morris, Percy Liang, Robb Willer, Michael S. Bernstein
The paper introduces an Evidence-Traceable Dynamic Interviewer Architecture that uses a locally hosted Large Language Model to adapt interview questions in real time based on participants’ expertise. It features a five‑module prompt‑driven design that profiles expertise, generates iterative questions, and maintains conversational continuity. In a study with 246 participants, the system achieved 78.9% agreement with self‑reported expertise and high ratings for relevance, engagement, and satisfaction.
By Aisvarya Adeseye, Jouni Isoaho, Adeyemi Adeseye, Seppo Virtanen, Mohammad Tahir
arXiv:2610.16050v1 Announce Type: new
Abstract: Personality assessment from asynchronous video interviews (AVIs) plays a crucial role in recruitment by enabling standardized and behaviorally grounded...
By Wei Shan, Tianyi Zhang, Yuan Zong, Wenming Zheng
The paper introduces an Evidence-Traceable Dynamic Interviewer Architecture that uses a locally hosted Large Language Model to adapt interview questions in real time based on participants’ expertise. It features a five‑module prompt‑driven design, including an Expertise Profiling module that achieved 78.9% exact agreement with self‑reported expertise and a Generate Iterative Questions module that showed a strong expertise‑complexity association. In a study with 246 participants, the system received high ratings for relevance, engagement, and satisfaction, indicating that its adaptive components functioned as intended.