3 papers
cs.CL2026
Adaptive Interviewing for Persona Simulation in LLMs: Evidence-Grounded Reasoning Improves Decision Alignment
Ruoxi Su, Yuhan Liu, Jingyu Hu
Accurately simulating the decisions of a specific individual remains challenging for large language models (LLMs), partly because persona information is often provided as static de…
cs.HC2026
How can LLMs Support Policy Researchers? Evaluating an LLM-Assisted Workflow for Large-Scale Unstructured Data
Yuhan Liu, Shuyao Zhou, Jakob Kaiser +5
Policy researchers need scalable ways to surface public views, yet they often rely on interviews, listening sessions, and surveys-analyzed thematically-that are slow, expensive, an…
cs.CL2026
How Far Are We? Systematic Evaluation of LLMs vs. Human Experts in Mathematical Contest in Modeling
Yuhang Liu, Heyan Huang, Yizhe Yang +3
Large language models (LLMs) have achieved strong performance on reasoning benchmarks, yet their ability to solve real-world problems requiring end-to-end workflows remains unclear…