LLM-Simulated Datasets

• LLM-simulated datasets are synthetic data generated by LLMs using role-based prompts and structured contexts to model complex data distributions.
• They facilitate applications across social simulation, education, and safety evaluation by enabling scalable prototyping and privacy-preserving analysis.
• Rigorous validation, uncertainty quantification, and expert review are essential to ensure the fidelity and ethical use of these datasets.

A dataset is termed “LLM-simulated” when its constituent data instances—whether they are survey responses, agent behaviors, sensor streams, code, or structured attributes—are generated de novo by one or more LLMs, typically under carefully engineered role-based prompts, sometimes supplemented with structured context, domain constraints, or human-in-the-loop validation. LLM-simulated datasets are increasingly prevalent in domains such as social simulation, agent-based modeling, psychometrics, synthetic education data, scenario-based evaluation, and data augmentation for high-dimensional or privacy-sensitive tasks. The primary objectives of these datasets are scalable prototyping, controllable diversity, privacy preservation, and the capacity to model phenomena beyond the reach of direct empirical measurement.

1. Foundations and Formal Properties of LLM-Simulated Datasets

LLM-simulated datasets are defined by the process in which an LLM acts as a generative model that produces synthetic samples in response to prompts that encode aspects of the data distribution, agent role, environmental conditions, or dialogue context. The generative process can be formalized as producing samples $x \sim Q(\cdot~|~C,p)$, where $C$ encodes structured context (e.g., demographic profile, environmental metadata, agent memory) and $p$ is the prompt or system message embedding task guidelines and behavioral constraints.

Synthetic data generated this way are typically not i.i.d. samples from the real-world target distribution $P(x)$, but rather realizations from the LLM-parameterized distribution $Q(x)$. The degree of alignment between $Q$ and $P$ is application-specific and must be empirically validated. In survey simulation, this misalignment is measured as $\Delta(q) = \|T[P(\cdot|q)] - T[Q(\cdot|q,p)]\|$, where $T$ is a target functional such as the mean or empirical distribution of interest (Huang et al., 25 Feb 2025).

A critical property of LLM-simulated data is the capacity to encode high-level dependencies and substantive reasoning in structured contexts, even when fine-grained statistical properties may diverge from empirical distributions. Contemporary research emphasizes rigorous benchmarking and uncertainty quantification to distinguish group-level correspondence from individual-level discrepancies (Cipriani et al., 2 Dec 2025, Huang et al., 25 Feb 2025).

2. Frameworks and Simulation Methodologies

LLM-simulated dataset creation typically follows a structured multi-stage pipeline. Key elements include:

• Agent-Based Simulation: LLMs are role-conditioned (often as agents) to simulate exchanges, behaviors, or decisions. For example, in peer review simulation, three reviewers, an author, and a senior reviewer role-play multi-round debates, with each stage instantiated via carefully parameterized instructions (Li et al., 11 Nov 2025). Similarly, agent-based frameworks model senators, legal actors, or virtual household agents, often paired with memory systems and turn-by-turn state transitions (Baker et al., 26 Jun 2024, Wang et al., 28 Oct 2025, Lin et al., 23 May 2025).
• Prompt Engineering and Conditioning: Systematic use of prompt templates ensures consistent task framing, response format, and adherence to domain conventions. Feature-conditional or context-enhanced prompt schemes facilitate fine control over sampled attributes, as in feature-conditional tabular data synthesis (Nguyen et al., 29 Oct 2024), partial attribute survey simulation (Zhao et al., 8 Sep 2025), and roleplay-based dialogue (Louie et al., 1 Jul 2024).
• Scenario Sampling and Data Fusion: For environmental and world-modeling simulations, pipeline stages include multi-source feature aggregation (e.g., geospatial, structural, socioeconomic, and visual features for disaster impact prediction (Li et al., 2 Jun 2025)), followed by decomposition into input fields for the LLM.
• Structured Output and Graph Encoding: Downstream applications often require converting raw LLM-generated text into formal structures, such as heterogeneous graphs (for argumentative debates (Li et al., 11 Nov 2025)), table schemas, or event logs linked via inter-entity or temporal relations.
• Automated Postprocessing: Includes compilation and filtering for format or semantic validity (e.g., correct code compilation (Leinonen et al., 1 Nov 2024)), aggregation of sampled responses, and dimensionality reduction or label assignment via further LLM calls or validation loops.

3. Quality Control, Uncertainty, and Validation Metrics

Rigorous validation is essential due to inherent domain and alignment biases in LLM outputs:

• Expert and Crowd Validation: Independent annotators assess realism, coverage, and correctness, often reporting inter-annotator agreement measures (e.g., Fleiss’s $\kappa=0.81$ for debate triple extraction (Li et al., 11 Nov 2025)), domain correctness, and believability via structured scales (Baker et al., 26 Jun 2024, Kashani, 30 Dec 2024).
• Fidelity and Distributional Alignment: Quantitative comparison with real-world baselines uses metrics such as KL divergence, Wasserstein distance for marginals, nonparametric statistical tests (Kruskal–Wallis, Mann–Whitney U), and coverage tests for confidence intervals (Huang et al., 25 Feb 2025, Cipriani et al., 2 Dec 2025, Tang et al., 20 May 2025).
• Uncertainty Quantification: The introduction of adaptive sample size selection balances confidence set width with coverage guarantees, operationally quantifying the degree of LLM–human alignment (Huang et al., 25 Feb 2025).
• Structural and Latent Validation: Advanced causal representation learning or independence testing can be used to verify if LLM-simulated trait or behavior scores exhibit genuine cross-modal dependency with external observational modalities (e.g., facial and biographical data in PersonaX (Li et al., 14 Sep 2025)).
• Internal Consistency and Principle Adherence: For interactive and social simulation, pipelines enforce “principle adherence,” reformatting or rewriting agent outputs to match explicit domain principles or behavioral rules (Louie et al., 1 Jul 2024).

4. Representative Studies and Domains of Application

LLM-simulated datasets underpin experimentation in a diverse range of high-impact tasks, including:

• Peer Review and Scholarly Debates: Reviewer–author multi-agent debate simulation for formal argument extraction and graph reasoning (Li et al., 11 Nov 2025).
• Psychometric and Survey Emulation: In silico scale development with factor analysis, group-level invariance testing, and formal comparison with human data (Cipriani et al., 2 Dec 2025, Zhao et al., 8 Sep 2025, Huang et al., 25 Feb 2025).
• Education: Privacy-preserving generation of buggy student code submissions and distributional benchmarking against real student work (Leinonen et al., 1 Nov 2024).
• Smart Home and Sensor Data: Simulation of human routines and sensor data via LLM-driven persona/routine generation and execution in simulated 3D environments (Leng et al., 13 Jun 2025).
• Social and Economic Simulation: Virtual legislative chambers, legal societies, macroeconomic expectation formation, and large-scale behavioral annotation using LLM-driven agents (Baker et al., 26 Jun 2024, Wang et al., 28 Oct 2025, Lin et al., 23 May 2025, Li et al., 14 Sep 2025).
• Scientific Benchmarks: Large instruction-following datasets for technical domains (e.g., quantum computing problem–solution pairs (Kashani, 30 Dec 2024)).
• Safety Evaluation: Multi-stage simulated agent conversations to probe LLM safety properties at different developmental stages (Murali et al., 7 Oct 2025).

5. Limitations, Risks, and Best Practices

Despite rapid progress, LLM-simulated datasets face well-documented limitations:

• Distributional Shift and Individual-Level Divergence: While group-level structure (e.g., factor models, attribute marginals) is often faithfully reproduced, synthetic data systematically diverges from empirical individual-level statistics—correlations, score variances, and distributional tails may be mismatched (Cipriani et al., 2 Dec 2025, Wang et al., 28 Oct 2025, Leinonen et al., 1 Nov 2024).
• Prompt and Domain Sensitivity: Data quality is strongly dependent on prompt engineering, in-context sampling strategies, and the specific LLM architecture/version deployed.
• Unmodeled Bias and Data Pollution: Pretraining distributional bias, or contamination in LLM training corpora, can yield spurious or overfitted synthetic patterns, especially in socially sensitive or policy domains (Cipriani et al., 2 Dec 2025).
• Ethical Hazards: Inadequate controls can allow synthetic datasets to be subverted for persuasion, targeted messaging, or algorithmic disinformation (Cipriani et al., 2 Dec 2025).
• Privacy Considerations: The privacy advantage of LLM-simulated data is conditional on the use of aggregate summaries for conditioning; direct regeneration from training data can invalidate syntheticity (Tang et al., 20 May 2025).

Standard best practices include thorough statistical benchmarking, rigorous annotation and error checking, explicit documentation of assumptions, and human-in-the-loop verification for realistic and ethical data creation.

6. Generalization, Reproducibility, and Future Directions

LLM-simulated dataset construction is extending beyond conventional format limits and easily adapts to new modalities and schema by design:

• Type-Agnostic Adaptability: Unified prompt and summarization protocols accommodate structured/unstructured entries, mixed modalities, and variable-sized entities (Tang et al., 20 May 2025, Li et al., 14 Sep 2025).
• Causal and Multimodal Expansion: Recent approaches use causal representation learning and multimodal trait inference to move beyond black-box embedding strategies (Li et al., 14 Sep 2025).
• Uncertainty-Aware Methodology: Sample size adaptivity and calibration strategies ensure coverage and operational performance guarantees, supporting robust deployment in policy and basic science (Huang et al., 25 Feb 2025).
• Open Reproducibility: Many frameworks release detailed code, simulation protocols, JSON schemas, and pre-generated synthetic corpora for community benchmarking and extension (Li et al., 11 Nov 2025, Cipriani et al., 2 Dec 2025, Leinonen et al., 1 Nov 2024, Leng et al., 13 Jun 2025).

A plausible implication is that LLM-simulated datasets will become essential infrastructure for rapid prototyping, privacy-compliant analysis, and agent-based research, provided that community standards for documentation, benchmark validation, and ethical guardrails continue to develop in proportion to their expanding influence across scientific, social, and technical disciplines.