Richer persona descriptions in LLMs cause systematic contraction of representational and behavioral diversity, with simple age-gender prompts outperforming complex ideal customer profiles in downstream accuracy.
Adjunct Proceedings of the 33rd ACM Conference on User Modeling, Adaptation and Personalization , pages =
6 Pith papers cite this work. Polarity classification is still indexing.
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2026 6verdicts
UNVERDICTED 6representative citing papers
Centralized matching mechanisms outperform free negotiation in stability and efficiency with LLM agents, who also report preferences truthfully more often than humans, though not always in line with strategy-proofness predictions.
Interventions in LLM-simulated user experiments induce distribution shifts in latent attributes that create confounding bias, diagnosable with negative control outcomes and partially mitigated by adding setting-relevant persona details.
LLM warm-starts for bandits remain better than cold-starts up to roughly 30% random label noise but increase regret under systematic misalignment, with a derived sufficient condition on prior error that predicts when the warm-start helps.
LLM digital personas improve alignment with human survey response distributions for stable attributes but remain limited for individual prediction and fail to recover multivariate respondent structure.
LLM agents display limited alignment with human emotional responses to red tape across cultures, performing worse in Eastern contexts, while cultural prompting offers little improvement.
citing papers explorer
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How Well Do Large Language Models Capture Human Personality?
Richer persona descriptions in LLMs cause systematic contraction of representational and behavioral diversity, with simple age-gender prompts outperforming complex ideal customer profiles in downstream accuracy.
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Do Matching Mechanisms Work with LLM Agents?
Centralized matching mechanisms outperform free negotiation in stability and efficiency with LLM agents, who also report preferences truthfully more often than humans, though not always in line with strategy-proofness predictions.
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The Illusion of Intervention: Your LLM-Simulated Experiment is an Observational Study
Interventions in LLM-simulated user experiments induce distribution shifts in latent attributes that create confounding bias, diagnosable with negative control outcomes and partially mitigated by adding setting-relevant persona details.
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Jump Start or False Start? A Theoretical and Empirical Evaluation of LLM-initialized Bandits
LLM warm-starts for bandits remain better than cold-starts up to roughly 30% random label noise but increase regret under systematic misalignment, with a derived sufficient condition on prior error that predicts when the warm-start helps.
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When Can Digital Personas Reliably Approximate Human Survey Findings?
LLM digital personas improve alignment with human survey response distributions for stable attributes but remain limited for individual prediction and fail to recover multivariate respondent structure.
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Cross-Cultural Simulation of Citizen Emotional Responses to Bureaucratic Red Tape Using LLM Agents
LLM agents display limited alignment with human emotional responses to red tape across cultures, performing worse in Eastern contexts, while cultural prompting offers little improvement.