Demographic Prompting at Scale: When More Attributes Hurt LLM–Human Agreement

Jul 14, 2026

A new preprint systematically varies demographic attributes in prompts across five tasks and five open-source LLMs, finding that alignment with human annotations peaks when using one to three high-signal attributes, but degrades when all attributes are included. The study also shows that the effectiveness of demographic prompting depends on the quality of attribute signals, the nature of the task, and the model architecture. Neuron probing further reveals that only coherent annotation signals lead to alignment gains, and that activation volume alone does not guarantee steerability.

Why it matters: This work provides empirical evidence that more demographic information in prompts does not always improve LLM alignment with human judgments, offering practical guidance for prompt design.

Full story at: arXiv Computation and Language ↗