arXiv:2606. 30660v1 Announce Type: cross Abstract: Collecting reliable social data from low-literacy populations remains a persistent challenge, particularly when surveys involve sensitive topics and marginalized communities.
arXiv:2608.22582v1 Announce Type: cross Abstract: Large-scale population surveys are essential for generating robust social and scientific insights, yet they face significant challenges, including de...
arXiv:2610.23149v1 Announce Type: cross Abstract: Large language model chatbots have been proposed as AI-interviewers for collecting qualitative interview data at scale, potentially bridging social s...
arXiv:2605.10659v3 Announce Type: replace Abstract: Digital personas powered by Large Language Models (LLMs) are increasingly proposed as substitutes for human survey respondents, yet it remains uncl...
arXiv:2607. 10628v1 Announce Type: cross Abstract: We present Anamnesis, an interactive system for demographically controllable survey simulation using large language models.
InterviewPlayground is a simulation environment designed to evaluate AI interviewers by using simulated participants whose behaviors are grounded in social theory. The platform generates an InterviewReportCard that assesses AI interviewers across validated measures. Empirical validation shows that performance in InterviewPlayground predicts human study outcomes with a Pearson correlation of 0.86, supporting its use for assessing AI interviewer performance and failure modes.
ExpertIVS is a framework that uses 14 sociological expert agents to interpret World Values Survey responses, reconstructing individual value systems in a coherent, internally consistent manner rather than simply concatenating survey answers. It introduces a multi‑agent debate mechanism to assess LLM alignment with these value profiles during dynamic interactions. Experiments on 480 individuals from 12 countries show a 90.78% value restoration fidelity and a 5.3% improvement in value generalization over baseline methods, while also demonstrating strong personality discriminability and behavioral consistency.
InterviewPlayground is a simulation environment designed to evaluate AI interviewers by using simulated participants whose behaviors are grounded in social theory. The platform generates an InterviewReportCard that assesses AI interviewers across validated measures. Empirical validation shows a strong correlation (average Pearson 0.86) between simulation results and real human studies, confirming the environment’s predictive validity.
arXiv:2603. 00059v3 Announce Type: replace-cross Abstract: How well can AI-derived synthetic research data replicate the responses of human participants?
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.
The study introduces a World Values Survey–grounded simulation framework to test whether large language model agents can faithfully represent diverse human value systems. In about 4,000 conversations with 1,200 personas across three models, more than half of the agents failed to express their assigned value profiles from the start, and only 2–7% drifted over time. The results show systematic deviations from the intended value distributions and reveal that simulated dialogues differ from human discussions in their balance of stylistic consistency and semantic diversity.
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.
arXiv:2607. 26236v1 Announce Type: cross Abstract: AI-generated counterspeech offers a scalable and effective strategy to mitigate online toxicity by promoting more constructive dialogue.