39 lines
2.6 KiB
Markdown
39 lines
2.6 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: unprocessed
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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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---
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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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