---
type: source
title: "Operationalizing Pluralistic Values in LLM Alignment"
authors: ["Park et al."]
url: https://arxiv.org/abs/2511.14476
date: 2025-11-01
processed_date: 2026-03-11
status: processed
---

# Operationalizing Pluralistic Values in LLM Alignment

**Authors:** Park et al.  
**Published:** November 2025  
**Source:** arXiv:2511.14476

## Summary

Empirical study demonstrating that demographic composition of alignment training data produces measurable behavioral differences in LLMs. N=1,095 participants across political ideology, age, gender, and education provided 27,375 preference ratings. Models trained on different demographic subgroups showed statistically significant differences (3-5 percentage points) on metrics including emotional awareness, political neutrality, and creativity.

## Key Findings

- Liberal vs. Conservative training data produced 5.0pp difference in emotional awareness
- 4.7pp difference in political neutrality metrics
- 3.4pp difference in creativity scores
- Effect sizes comparable to performance gaps between model generations

## Extraction Notes

Could not access full paper — extraction based on search summary and agent notes. Effect sizes and methodological details should be verified when full text becomes available.

## Claims Generated

- [[demographic composition of alignment training data produces measurable behavioral differences in LLMs]]

## Enrichments

- [[community-centered design produces better outcomes than user-centered design for collective-use systems]] — Added evidence that demographic composition affects AI behavior (2026-03-11)
- [[some disagreements are permanently irreducible because they stem from genuine value differences not information gaps and systems must map rather than eliminate them]] — Added empirical demonstration that value differences produce different AI behaviors (2026-03-11)