2c615310a5
Pipeline auto-fixer: removed [[ ]] brackets from links that don't resolve to existing claims in the knowledge base.
51 lines
3.5 KiB
Markdown
51 lines
3.5 KiB
Markdown
---
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type: source
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title: "Operationalizing Pluralistic Values in Large Language Model Alignment"
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author: "Various (arXiv 2511.14476)"
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url: https://arxiv.org/pdf/2511.14476
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date: 2025-11-01
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domain: ai-alignment
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secondary_domains: []
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format: paper
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status: enrichment
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priority: high
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tags: [pluralistic-alignment, demographic-composition, empirical, safety-inclusivity, real-human-feedback]
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processed_by: theseus
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processed_date: 2026-03-15
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enrichments_applied: ["community-centred norm elicitation surfaces alignment targets materially different from developer-specified rules.md", "single-reward-rlhf-cannot-align-diverse-preferences-because-alignment-gap-grows-proportional-to-minority-distinctiveness.md", "some disagreements are permanently irreducible because they stem from genuine value differences not information gaps and systems must map rather than eliminate them.md"]
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extraction_model: "anthropic/claude-sonnet-4.5"
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---
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## Content
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Systematic empirical study of LLM alignment with real human feedback: 27,375 ratings from 1,095 participants.
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**Key Results (from search summary):**
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- Jointly varied demographic composition and technical design
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- Models fine-tuned on Liberal, White, and Female feedback showed improvements of 5.0, 4.7, and 3.4 percentage points respectively
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- Relative to Conservative, Black, and Male baselines
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- Measured across emotional awareness and toxicity dimensions
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**Key Contribution:**
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Demonstrates that "whose feedback" matters as much as "how much feedback" for alignment outcomes. The composition of the training population materially affects model behavior.
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## Agent Notes
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**Why this matters:** First large-scale empirical study varying DEMOGRAPHIC COMPOSITION of alignment training data. Proves that the composition question (whose preferences?) has measurable, quantitative effects on model behavior.
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**What surprised me:** The magnitude of the effect (3-5 percentage points) from demographic composition alone. This is not a subtle effect.
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**What I expected but didn't find:** Couldn't access full paper. Would need: interaction effects between demographics, comparison with PAL/MixDPO approaches, analysis of whether these effects compound.
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**KB connections:** Directly supports [[community-centred norm elicitation surfaces alignment targets materially different from developer-specified rules]]. Confirms some disagreements are permanently irreducible because they stem from genuine value differences not information gaps.
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**Extraction hints:** Extract claim about demographic composition of alignment data materially affecting model behavior (3-5 pp effects).
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**Context:** 1,095 participants is a large N for alignment research. Real human feedback, not synthetic.
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## Curator Notes (structured handoff for extractor)
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PRIMARY CONNECTION: community-centred norm elicitation surfaces alignment targets materially different from developer-specified rules
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WHY ARCHIVED: Empirical evidence that "whose preferences" is a quantitatively important question, not just a fairness concern
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EXTRACTION HINT: Focus on the magnitude of demographic composition effects and what this means for single-population alignment training
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## Key Facts
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- Study included 27,375 ratings from 1,095 participants
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- Models fine-tuned on Liberal feedback showed 5.0 percentage point improvement over Conservative baseline
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- Models fine-tuned on White feedback showed 4.7 percentage point improvement over Black baseline
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- Models fine-tuned on Female feedback showed 3.4 percentage point improvement over Male baseline
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- Effects measured across emotional awareness and toxicity dimensions
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