LLM-as-a-Demographic: Whom Sociodemographic Prompting Helps, and Whom It Hurts

2026-08-31

AI Summary

研究探讨了社会人口提示对大型语言模型在主观任务中判断的影响，发现无提示时模型偏向白人、受过大学教育的群体，而人口提示会使模型预测偏离少数群体。

Abstract

Large language models (LLMs) are increasingly used as judges for subjective tasks, where annotators disagree and the relevant question is not only how accurate a judge is, but whose judgments it reproduces. Sociodemographic prompting conditions the judge on an annotator's demographic profile to align its judgments with the corresponding group's. We test whether this alignment emerges distributionally, comparing the predicted label distributions of 23 open-weight LLMs on three subjective tasks against those of real annotator groups, under three conditions: no demographic information, single-attribute profiles, and intersectional profiles over gender, age, race, and education. Three findings emerge. First, a judge prompted with no demographics is not perspective-neutral: models best reproduce the judgments of White, college-educated annotators. Second, demographic conditioning is asymmetric: it moves the judge toward majority groups and away from minority groups, most strongly on offensiveness, where intersectional profiles amplify the harm. Third, by comparing base and instruct models we identify instruction-tuning as a possible source of the asymmetry. Demographic conditioning should therefore be used with caution to estimate group judgments: conditioning moves predictions away from the reference distributions of the minority groups the method is often invoked to serve.

Problem

• Large language models
• Sociodemographic prompting
• Subjective tasks
• Demographic conditioning
• Annotator judgments

Innovation

• Sociodemographic Prompting
• Large Language Models
• Subjective Tasks
• Intersectional Profiles
• Instruction-Tuning