Consequential Behaviour and Representational Fairness in the Validation of Synthetic Research
cs.CL, cs.CY
Submitted: 2026-09-23
Updated: 2026-10-06
Terminology
Sources
- Using Large Language Models to Simulate Multiple Humans and Replicate Human Subject Studies
- LLM Social Simulations Are a Promising Research Method
- Overstating Attitudes, Ignoring Networks: LLM Biases in Simulating Misinformation Susceptibility
- The Challenge of Using LLMs to Simulate Human Behavior: A Causal Inference Perspective
- Valid Survey Simulations with Limited Human Data: The Roles of Prompting, Fine-Tuning, and Rectification
- Can LLMs Simulate Personas with Reversed Performance? A Systematic Investigation for Counterfactual Instruction Following in Math Reasoning Context
- The Illusion of Intervention: Your LLM-Simulated Experiment is an Observational Study
- Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization
- Whose Opinions Do Language Models Reflect?
- LLM Agents Grounded in Self-Reports Enable General-Purpose Simulation of Individuals
- Evaluating the Social Impact of Generative AI Systems in Systems and Society
- Political Compass or Spinning Arrow? Towards More Meaningful Evaluations for Values and Opinions in Large Language Models
- Random Silicon Sampling: Simulating Human Sub-Population Opinion Using a Large Language Model Based on Group-Level Demographic Information
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