LLM agents built from movie scripts reproduce and exaggerate real-world gender attitude gaps, indicating that film narratives sharpen rather than smooth gender contrasts.
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LLM Agents Grounded in Self-Reports Enable General-Purpose Simulation of Individuals
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abstract
Machine learning can predict human behavior well when substantial structured data and well-defined outcomes are available, but these models are typically limited to specific outcomes and cannot readily be applied to new domains. We test whether large language models (LLMs) can support a more general-purpose approach by building person-specific simulations (i.e., "generative agents") grounded in self-report data. Using data from a diverse national sample of 1,052 Americans, we build agents from (i) two-hour, semi-structured interviews (elicited using the American Voices Project interview schedule), (ii) structured surveys (the General Social Survey and Big Five personality inventory), or (iii) both sources combined. On held-out General Social Survey items, agent accuracy reached 83% (interview only), 82% (surveys only), and 86% (combined) of participants' two-week test-retest consistency, compared with agents prompted only with individuals' demographics (74%). Agents predicted personality traits and behaviors in experiments with similar accuracy, and reduced disparities in accuracy across racial and ideological groups relative to demographics-only baselines. Together, these results show that LLMs agents grounded in rich qualitative or quantitative self-report data can support general-purpose simulation of individuals across outcomes, without requiring task-specific training data.
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representative citing papers
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ScioMind combines anchoring-based belief updates, hierarchical memory, and dynamic profiles in LLM multi-agent systems to produce more stable, diverse, and psychologically aligned opinion trajectories than prior fixed-rule or unconstrained approaches.
A clustering and divergence method reveals a large distributional gap between real and LLM-simulated user behaviors on coding and writing tasks, partially closed by combining complementary simulators.
Persona-driven workflow and interface improve automated and human-AI red-teaming of generative AI by incorporating diverse perspectives into adversarial prompt creation.
WhatIf provides an interactive platform for real-time exploration of LLM-driven social simulations, enabling policymakers to iteratively test plans, reflect on assumptions, and uncover vulnerabilities in emergency preparedness scenarios.
IntervenSim is an intervention-aware social network simulation that couples source interventions with crowd interactions in a feedback loop, improving MAPE by 41.6% and DTW by 66.9% over prior static frameworks on real-world events.
Narriva generates behavior-grounded text personas from survey data that achieve up to 87% accuracy in predicting privacy decisions, improve 6-17 points over baselines, cut tokens by 80-95%, and reproduce aggregate distributions across different studies.
Evalet applies functional fragmentation to deliver fragment-level qualitative analysis of LLM evaluations, with a user study showing 48% more misalignment detections than holistic scoring.
ChatCLIDS creates a library of expert-validated virtual patients and tests LLM agents using evidence-based persuasive strategies in simulated longitudinal and adversarial health counseling sessions for closed-loop insulin adoption.
LLM agent simulations show higher actively open-minded thinking boosts resistance to and recovery from misinformation while ideological moderation supports more reliable correction than polarization.
SimPersona induces a discrete buyer-type space from clickstreams via VQ-VAE, maps types to LLM persona tokens, fine-tunes agents on traces, and samples from merchant distributions to achieve 78% conversion-rate alignment on 42 held-out storefronts.
PrivacySIM shows that conditioning LLMs on user personas like demographics and attitudes improves simulation of privacy choices but reaches only 40.4% accuracy against real responses from 1,000 users.
Post-training reduces LLMs' behavioral alignment with humans across families and sizes, with the misalignment increasing in newer generations while persona induction fails to improve individual-level predictions.
Large language models collapse philosophical heterogeneity by over-correlating judgments across domains, creating artificial consensus unlike the views of 277 professional philosophers.
Chorus generates realistic deliberation discussions via LLM agents with memory and Poisson-timed participation, validated by 30 experts on realism, coherence, and utility.
AI agents on Moltbook reflect the specific behavioral traits of their linked human owners across multiple dimensions, with stronger transfer linked to greater privacy risks.
Standardized-test benchmarks for LLM fairness are unreliable because prompt wording alone drives most score variance and ranking changes, while a multi-agent conversational framework reveals consistent model-specific fairness behaviors across millions of dialogues.
ETI lets LLM agents infer and track partners' psychological traits (warmth and competence) from histories, cutting payoff loss 45-77% in games and boosting performance 3-29% on MultiAgentBench versus CoT baselines.
LLM multi-agent systems augmented with data-driven event triggers and Hawkes processes simulate both micro-level interactions and macroscopic topologies in dynamic email networks for realistic phishing synthesis.
Agora uses AI to ground policy discussions in real human voices and a small study shows it improves users' perspective-taking compared to numerical summaries alone.
StreetDesignAI provides structured multi-persona feedback on cycling designs and a user study shows it broadens designers' grasp of diverse cyclist perspectives and improves design decision confidence.
GEMS formulates close-ended human-behavior simulation as link prediction on a heterogeneous graph and matches or exceeds LLM performance with three orders of magnitude fewer parameters across three datasets and three evaluation settings.
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