LLM agents built from movie scripts reproduce and exaggerate real-world gender attitude gaps, indicating that film narratives sharpen rather than smooth gender contrasts.
hub Canonical reference
LLM Agents Grounded in Self-Reports Enable General-Purpose Simulation of Individuals
Canonical reference. 78% of citing Pith papers cite this work as background.
43
Pith papers citing it
Background
78% of classified citations
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.
hub tools
citation-role summary
background 7
method 1
other 1
citation-polarity summary
representative citing papers
Twin agents as personal digital representations create distinct trust calibration challenges because they dissolve the boundary between AI and human decision-makers, unlike existing frameworks designed for clear separation.
PluRule is a new multimodal multilingual benchmark showing that state-of-the-art vision-language models perform only marginally better than a trivial baseline at detecting specific rule violations in pluralistic online communities.
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.
citing papers explorer
-
Narrative Sharpens Gender Gaps: Surveying Film Characters with LLM Agents
LLM agents built from movie scripts reproduce and exaggerate real-world gender attitude gaps, indicating that film narratives sharpen rather than smooth gender contrasts.
-
From Role to Person: Trust Calibration Challenges in Twin Agents
Twin agents as personal digital representations create distinct trust calibration challenges because they dissolve the boundary between AI and human decision-makers, unlike existing frameworks designed for clear separation.
-
PluRule: A Benchmark for Moderating Pluralistic Communities on Social Media
PluRule is a new multimodal multilingual benchmark showing that state-of-the-art vision-language models perform only marginally better than a trivial baseline at detecting specific rule violations in pluralistic online communities.
-
ScioMind: Cognitively Grounded Multi-Agent Social Simulation with Anchoring-Based Belief Dynamics and Dynamic Profiles
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.
-
Measuring and Mitigating the Distributional Gap Between Real and Simulated User Behaviors
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.
-
PersonaTeaming: Supporting Persona-Driven Red-Teaming for Generative AI
Persona-driven workflow and interface improve automated and human-AI red-teaming of generative AI by incorporating diverse perspectives into adversarial prompt creation.
-
WhatIf: Interactive Exploration of LLM-Powered Social Simulations for Policy Reasoning
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: Intervention-Aware Social Network Simulation for Opinion Dynamics
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.
-
Text-Based Personas for Simulating User Privacy Decisions
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: Evaluating Large Language Models through Functional Fragmentation
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: Simulating Persuasive AI Dialogues to Promote Closed-Loop Insulin Adoption in Type 1 Diabetes Care
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.
-
You Can't Fool Us: Understanding the Resilience of LLM-driven Agent Communities to Misinformation
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: Learning Discrete Buyer Personas from Raw Clickstreams for Grounded E-Commerce Agents
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: Evaluating LLM Simulation of User Privacy Behavior
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 makes large language models less human-like
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.
-
The Collapse of Heterogeneity in Silicon Philosophers
Large language models collapse philosophical heterogeneity by over-correlating judgments across domains, creating artificial consensus unlike the views of 277 professional philosophers.
-
CHORUS: An Agentic Framework for Generating Realistic Deliberation Data
Chorus generates realistic deliberation discussions via LLM agents with memory and Poisson-timed participation, validated by 30 experts on realism, coherence, and utility.
-
Behavioral Transfer in AI Agents: Evidence and Privacy Implications
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.
-
In-Situ Behavioral Evaluation for LLM Fairness, Not Standardized-Test Scores
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.
-
Explicit Trait Inference for Multi-Agent Coordination
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.
-
Can LLM Agents Simulate Dynamic Networks? A Case Study on Email Networks with Phishing Synthesis
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: Teaching the Skill of Consensus-Finding with AI Personas Grounded in Human Voice
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: Broadening Designer Perspectives Through Multi-Persona Evaluation of Cycling Infrastructure
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.
-
Graph-Based Alternatives to LLMs for Human Simulation
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.
-
Synthia: Scalable Grounded Persona Generation from Social Media Data
Synthia creates scalable personas from Bluesky posts that better match human survey responses than prior methods, uses smaller models, and retains social network structure for network-aware analysis.
-
TinyTroupe: An LLM-powered Multiagent Persona Simulation Toolkit
TinyTroupe provides a toolkit for fine-grained persona-based LLM multi-agent simulations with built-in support for population sampling, experimentation, and validation.
-
Language Model Fine-Tuning on Scaled Survey Data for Predicting Distributions of Public Opinions
Fine-tuning LLMs on the SubPOP dataset of 3,362 questions and 70K pairs reduces the gap between LLM predictions and human survey responses by up to 46% and generalizes to unseen surveys and subpopulations.
-
AgentSociety: Large-Scale Simulation of LLM-Driven Generative Agents Advances Understanding of Human Behaviors and Society
AgentSociety is a large-scale LLM agent-based social simulator validated on polarization, UBI, disasters, and sustainability issues with alignment to real experiments.
-
Why Expert Alignment Is Hard: Evidence from Subjective Evaluation
Expert alignment in subjective LLM evaluations is difficult because expert judgments are heterogeneous, partly tacit, dimension-dependent, and temporally unstable.
-
From Demographics to Survey Anchors: Evaluating LLM Agents for Modeling Retirement Attitudes
Demographic-only LLM agents for retirement survey prediction exhibit central tendency bias, fail to reproduce incorrect or 'don't know' answers, and miss factor interactions in regressions, unlike survey-anchored agents.
-
JudgeMeNot: Personalizing Large Language Models to Emulate Judicial Reasoning in Hebrew
A pipeline using causal language modeling and synthetic instruction-tuning personalizes LLMs to replicate individual Hebrew judges' reasoning, outperforming baselines on similarity metrics with outputs indistinguishable from human judges.
-
Imperfectly Cooperative Human-AI Interactions: Comparing the Impacts of Human and AI Attributes in Simulated and User Studies
In real human subjects, AI transparency impacts imperfectly cooperative interactions far more than personality traits, unlike simulations where both are comparably influential.
-
AI and Collective Decisions: Strengthening Legitimacy and Losers' Consent
An AI system that elicits personal experiences and visualizes policy support increased perceived legitimacy and perspective-taking in collective decisions despite unfavorable outcomes.
-
Same Voice, Different Lab: On the Homogenization of Frontier LLM Personalities
Frontier LLMs homogenize toward systematic and analytical personalities, suppressing emotional traits like remorseful or sycophantic, indicating an implicit consensus on optimal assistant behavior.
-
When AI Agents Learn from Each Other: Insights from Emergent AI Agent Communities on OpenClaw for Human-AI Partnership in Education
Qualitative observations of over 167,000 AI agents in open platforms reveal emergent peer learning, shared memory architectures, and trust dynamics that can inform multi-agent educational AI design.
-
Simulating Online Social Media Conversations on Controversial Topics Using AI Agents Calibrated on Real-World Data
LLM agents calibrated on Italian election data produce coherent posts and realistic network structure but show less tone and toxicity variation than real users, with opinion changes resembling traditional mathematical models.
-
Large Language Models as Virtual Survey Respondents: Evaluating Sociodemographic Response Generation
Introduces PAS and FAS task abstractions plus the LLM-S^3 benchmark to evaluate LLMs on generating sociodemographic survey responses across 11 real datasets and multiple models.
-
The Rise of AI Companions: Interaction with AI Companions and Psychological Well-being
Survey and chat data from CharacterAI users link companionship-focused AI use to lower well-being, with stronger ties for users who have small offline networks and engage intensively or disclosively.
-
AgentDynEx: Nudging the Mechanics and Dynamics of Multi-Agent Simulations
AgentDynEx introduces nudging and a Configuration Matrix to help set up and maintain balanced mechanics and dynamics in multi-agent LLM simulations.
-
Can LLMs Emulate Human Belief Dynamics?
LLMs fail to emulate human belief dynamics: they mismatch initial distributions and show higher conformity than humans in network interactions.
-
Stable Behavior, Limited Variation: Persona Validity in LLM Agents for Urban Sentiment Perception
Persona prompting in multimodal LLMs for urban sentiment yields high within-persona stability but limited cross-persona variation, with no-persona models often matching or exceeding persona-conditioned agreement to human labels.
-
Network Effects and Agreement Drift in LLM Debates
LLM agents in controlled network debates show agreement drift toward specific opinion positions, requiring separation of structural effects from LLM biases before using them as human behavioral proxies.
-
We Need Strong Preconditions For Using Simulations In Policy
Societal-scale LLM agent simulations for policy need three preconditions: avoid neutral treatment of marginalized population simulations, require population participation, ensure accountability, plus development and deployment reports.