5 citations · 11 across the 10 of their papers we have counts for
4 papers · 2 filters
SimBench: Benchmarking the Ability of Large Language Models to Simulate Human Behaviors
Tiancheng Hu, Joachim Baumann, Lorenzo Lupo +3
Large language model (LLM) simulations of human behavior have the potential to revolutionize the social and behavioral sciences, if and only if they faithfully reflect real human b…
Navigating the Alignment-Calibration Trade-off: A Pareto-Superior Frontier via Model Merging
Tiancheng Hu, Benjamin Minixhofer, Nigel Collier
The "alignment tax" of post-training is typically framed as a drop in task accuracy. We show it also involves a severe loss of calibration, making models overconfident, less reliab…
iNews: A Multimodal Dataset for Modeling Personalized Affective Responses to News
Tiancheng Hu, Nigel Collier
Understanding how individuals perceive and react to information is fundamental for advancing social and behavioral sciences and developing human-centered AI systems. Current approa…
When Personalization Meets Reality: A Multi-Faceted Analysis of Personalized Preference Learning
Yijiang River Dong, Tiancheng Hu, Yinhong Liu +2
While Reinforcement Learning from Human Feedback (RLHF) is widely used to align Large Language Models (LLMs) with human preferences, it typically assumes homogeneous preferences ac…