Research Paper#LLMs, Social Desirability Bias, Prompt Engineering, Silicon Sampling🔬 ResearchAnalyzed: Jan 3, 2026 19:41
Mitigating Social Bias in LLM-Based Population Simulations
Published:Dec 27, 2025 23:21
•1 min read
•ArXiv
Analysis
This paper addresses a crucial problem in the use of Large Language Models (LLMs) for simulating population responses: Social Desirability Bias (SDB). It investigates prompt-based methods to mitigate this bias, which is essential for ensuring the validity and reliability of LLM-based simulations. The study's focus on practical prompt engineering makes the findings directly applicable to researchers and practitioners using LLMs for social science research. The use of established datasets like ANES and rigorous evaluation metrics (Jensen-Shannon Divergence) adds credibility to the study.
Key Takeaways
- •LLMs exhibit Social Desirability Bias (SDB) when simulating population responses.
- •Prompt-based methods can mitigate SDB.
- •Reformulated prompts (neutral phrasing) are most effective.
- •Other methods (reverse-coding, priming, preamble) showed mixed or no benefit.
- •Findings improve the representativeness of LLM-based simulations.
Reference
“Reformulated prompts most effectively improve alignment by reducing distribution concentration on socially acceptable answers and achieving distributions closer to ANES.”