arXiv:2608. 14606v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as synthetic survey respondents, but existing evaluations ask whether answers look plausible at the individual level.
The study evaluates whether large language models (LLMs) used as synthetic personas can predict real audience responses to marketing copy. Using thousands of headline A/B tests from the Upworthy Research Archive, the authors compare a ten-persona panel grounded in real audience demographics to a no-persona zero‑shot baseline that asks the model for a typical reader’s click likelihood. Results show that the no‑persona baseline outperforms the persona‑based approach, with higher predictive validity and top‑1 accuracy, indicating that forcing the model to role‑play specific personas introduces bias and noise.
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...
arXiv:2606. 20205v1 Announce Type: new Abstract: Psychological instruments designed for humans are increasingly used to assign large language models (LLMs) stable psychological profiles that affect their usability, safety assessment, and use as proxies for human participants in research.
arXiv:2607.02368v4 Announce Type: replace-cross Abstract: Researchers are increasingly prompting language models with cultural personas so that they answer questionnaires as diverse human groups woul...
arXiv:2609.13773v1 Announce Type: new Abstract: LLM-as-a-Judge evaluators are increasingly used to score open-ended generation, yet a judge's correlation with human ratings on its development set may...
arXiv:2610.05094v1 Announce Type: cross Abstract: Researchers increasingly use Large Language Models as judges (LLM-as-a-judge) to evaluate model outputs. Yet there are no standards for how to design...
arXiv:2608.29455v1 Announce Type: cross Abstract: LLMs are increasingly used as human surrogates, often on the premise that richer persona data could make them substitutes or exploratory tools for sp...
arXiv:2606. 09843v3 Announce Type: replace-cross Abstract: Large language models (LLMs) give stable answers to personality questionnaires, yet these self-reports fail to predict how the models behave.
The study investigates whether large language models (LLMs) can accurately simulate individual human responses by testing the assumption that richer personas improve prediction fidelity. Across four public datasets, researchers found that LLMs largely predict item means but fail to capture individual deviations, particularly the stable, person‑by‑item signal that accounts for most variance. Even with fine‑tuning and varied models, LLMs recover only a small fraction of this nuanced signal, highlighting limitations in using them as precise human surrogates.
The paper investigates item-sensitivity—whether a language model’s choice depends on the specific input—in a forced-choice signalling task derived from the board game Deception: Murder in Hong Kong. Across seven models, two families, a post‑training ablation, and three scoring rules, every tested cell shows item‑sensitivity, yet many are statistically indistinguishable from random choice and some perform worse than random. The authors term this phenomenon "consistency without alignment" and argue it undermines evaluations that rely solely on item‑sensitivity, permutation consistency, or self‑consistency without an independent reference.
The paper introduces the latent space item response model (LSIRM) as a tool for uncovering how language models differ on specific benchmark items. By mapping 1,348 models across 5,761 BBH items and comparing scoring methods across BBH, GPQA, MMLU-Pro, and MuSR, the authors show that items with similar correct answers cluster together, especially when scored by option labels. LSIRM separates a model’s tendency to perform better on certain answer categories from other performance differences, revealing that small changes in scoring can dramatically alter model rankings.