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1.8 KiB
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39 lines
No EOL
1.8 KiB
Markdown
---
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type: source
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title: "Operationalizing Pluralistic Values in LLM Alignment"
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authors: ["Park et al."]
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url: https://arxiv.org/abs/2511.14476
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date: 2025-11-01
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processed_date: 2026-03-11
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status: processed
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---
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# Operationalizing Pluralistic Values in LLM Alignment
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**Authors:** Park et al.
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**Published:** November 2025
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**Source:** arXiv:2511.14476
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## Summary
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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.
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## Key Findings
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- Liberal vs. Conservative training data produced 5.0pp difference in emotional awareness
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- 4.7pp difference in political neutrality metrics
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- 3.4pp difference in creativity scores
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- Effect sizes comparable to performance gaps between model generations
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## Extraction Notes
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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.
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## Claims Generated
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- [[demographic composition of alignment training data produces measurable behavioral differences in LLMs]]
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## Enrichments
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- [[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)
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- [[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) |