arXiv AI
Sep 25

From Policy Documents to Structured Survey Responses: Evaluating Large Language Models for Policy Monitoring

The paper explores using large language models (LLMs) as AI respondents to convert policy documents into structured survey responses. It introduces a long-context in‑context learning pipeline that maps policy text to predefined survey categories such as policy instruments, target groups, and thematic areas, and includes a secondary LLM validation step. Evaluation on a multi‑country dataset shows high agreement (84‑95%) with human responses for structured indicators, though free‑text fields differ, indicating that hybrid human‑AI workflows can enhance policy monitoring efficiency while still requiring human oversight.

By Carolyn Cole, Matthias Deschryvere, Toqeer Ehsan, Arash Hajikhani
arXiv Computation and Language
Sep 3

When Persona Attributes Improve Population Alignment in Large Language Models

The paper investigates how persona prompting—using short textual descriptions of individuals—to align large language models (LLMs) with human survey responses. It examines the impact of selecting different persona attributes and finds that not all attribute combinations improve performance, suggesting that the variation in human responses to survey questions may explain mixed results. The study evaluates multiple attribute selection methods across four social surveys, two countries, six LLMs, and twenty prediction tasks, offering guidance on when persona prompting is beneficial and which attribute choices are most effective.

By Leon Fr\"ohling, Jens Rupprecht, Markus Strohmaier, Claudia Wagner
arXiv Computation and Language
Sep 16

Towards Detecting AI-Assisted Responses in Online Surveys

The paper introduces ASURRE, a benchmark dataset for detecting AI‑assisted responses in online surveys. It evaluates how different LLM usage strategies—ranging from full generation to persona‑grounded agentic completion—affect the performance of existing machine‑generated text detectors. The study finds that while naive AI usage is easily detected, more sophisticated persona‑grounded agents approach chance performance, yet still leave identifiable behavioural traces that can be aggregated to improve detection.

By Qizhou Wang, Bogdan Mamaev, Christopher Leckie