Large Language Models as Psychological Simulators: A Methodological Guide

• URL: http://arxiv.org/abs/2506.16702v1
• Date: Fri, 20 Jun 2025 02:45:23 GMT
• Title: Large Language Models as Psychological Simulators: A Methodological Guide
• Authors: Zhicheng Lin,
• Abstract summary: This article provides a framework for using large language models as psychological simulators across two primary applications.<n>For simulation, we present methods for developing psychologically grounded personas that move beyond demographic categories.<n>We address overarching challenges including prompt sensitivity, temporal limitations from training data cutoffs, and ethical considerations that extend beyond traditional human subjects review.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) offer emerging opportunities for psychological and behavioral research, but methodological guidance is lacking. This article provides a framework for using LLMs as psychological simulators across two primary applications: simulating roles and personas to explore diverse contexts, and serving as computational models to investigate cognitive processes. For simulation, we present methods for developing psychologically grounded personas that move beyond demographic categories, with strategies for validation against human data and use cases ranging from studying inaccessible populations to prototyping research instruments. For cognitive modeling, we synthesize emerging approaches for probing internal representations, methodological advances in causal interventions, and strategies for relating model behavior to human cognition. We address overarching challenges including prompt sensitivity, temporal limitations from training data cutoffs, and ethical considerations that extend beyond traditional human subjects review. Throughout, we emphasize the need for transparency about model capabilities and constraints. Together, this framework integrates emerging empirical evidence about LLM performance--including systematic biases, cultural limitations, and prompt brittleness--to help researchers wrangle these challenges and leverage the unique capabilities of LLMs in psychological research.

Related papers

• Anomaly Detection and Generation with Diffusion Models: A Survey [51.61574868316922]
  Anomaly detection (AD) plays a pivotal role across diverse domains, including cybersecurity, finance, healthcare, and industrial manufacturing.<n>Recent advancements in deep learning, specifically diffusion models (DMs), have sparked significant interest.<n>This survey aims to guide researchers and practitioners in leveraging DMs for innovative AD solutions across diverse applications.
  arXiv  Detail & Related papers  (2025-06-11T03:29:18Z)
• Adaptive Token Boundaries: Integrating Human Chunking Mechanisms into Multimodal LLMs [0.0]
  This research presents a systematic investigation into the parallels between human cross-modal chunking mechanisms and token representation methodologies.<n>We propose a novel framework for dynamic cross-modal tokenization that incorporates adaptive boundaries, hierarchical representations, and alignment mechanisms grounded in cognitive science principles.
  arXiv  Detail & Related papers  (2025-05-03T09:14:24Z)
• Humanizing LLMs: A Survey of Psychological Measurements with Tools, Datasets, and Human-Agent Applications [25.38031971196831]
  Large language models (LLMs) are increasingly used in human-centered tasks.<n>Assessing their psychological traits is crucial for understanding their social impact and ensuring trustworthy AI alignment.<n>This study aims to propose future directions for developing more interpretable, robust, and generalizable psychological assessment frameworks for LLMs.
  arXiv  Detail & Related papers  (2025-04-30T06:09:40Z)
• The potential -- and the pitfalls -- of using pre-trained language models as cognitive science theories [2.6549754445378344]
  We discuss challenges to the use of PLMs as cognitive science theories.<n>We review assumptions used by researchers to map measures of PLM performance to measures of human performance.<n>We end by enumerating criteria for using PLMs as credible accounts of cognition and cognitive development.
  arXiv  Detail & Related papers  (2025-01-22T05:24:23Z)
• PersLLM: A Personified Training Approach for Large Language Models [66.16513246245401]
  We propose PersLLM, a framework for better data construction and model tuning.<n>For insufficient data usage, we incorporate strategies such as Chain-of-Thought prompting and anti-induction.<n>For rigid behavior patterns, we design the tuning process and introduce automated DPO to enhance the specificity and dynamism of the models' personalities.
  arXiv  Detail & Related papers  (2024-07-17T08:13:22Z)
• LLM-driven Imitation of Subrational Behavior : Illusion or Reality? [3.2365468114603937]
  Existing work highlights the ability of Large Language Models to address complex reasoning tasks and mimic human communication. We propose to investigate the use of LLMs to generate synthetic human demonstrations, which are then used to learn subrational agent policies. We experimentally evaluate the ability of our framework to model sub-rationality through four simple scenarios.
  arXiv  Detail & Related papers  (2024-02-13T19:46:39Z)
• Systematic Biases in LLM Simulations of Debates [12.933509143906141]
  We study the limitations of Large Language Models in simulating human interactions.<n>Our findings indicate a tendency for LLM agents to conform to the model's inherent social biases.<n>These results underscore the need for further research to develop methods that help agents overcome these biases.
  arXiv  Detail & Related papers  (2024-02-06T14:51:55Z)
• User Behavior Simulation with Large Language Model based Agents [116.74368915420065]
  We propose an LLM-based agent framework and design a sandbox environment to simulate real user behaviors. Based on extensive experiments, we find that the simulated behaviors of our method are very close to the ones of real humans.
  arXiv  Detail & Related papers  (2023-06-05T02:58:35Z)
• Rethinking Model Evaluation as Narrowing the Socio-Technical Gap [47.632123167141245]
  We argue that model evaluation practices must take on a critical task to cope with the challenges and responsibilities brought by this homogenization.<n>We urge the community to develop evaluation methods based on real-world contexts and human requirements.
  arXiv  Detail & Related papers  (2023-06-01T00:01:43Z)
• Machine Psychology [54.287802134327485]
  We argue that a fruitful direction for research is engaging large language models in behavioral experiments inspired by psychology. We highlight theoretical perspectives, experimental paradigms, and computational analysis techniques that this approach brings to the table. It paves the way for a "machine psychology" for generative artificial intelligence (AI) that goes beyond performance benchmarks.
  arXiv  Detail & Related papers  (2023-03-24T13:24:41Z)
• DIME: Fine-grained Interpretations of Multimodal Models via Disentangled Local Explanations [119.1953397679783]
  We focus on advancing the state-of-the-art in interpreting multimodal models. Our proposed approach, DIME, enables accurate and fine-grained analysis of multimodal models.
  arXiv  Detail & Related papers  (2022-03-03T20:52:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.