teleo-codex/inbox/archive/ai-alignment/2025-11-00-operationalizing-pluralistic-values-llm-alignment.md

type	title	author	url	date	domain	secondary_domains	format	status	priority	tags	processed_by	processed_date	enrichments_applied	extraction_model	claims_extracted
source	Operationalizing Pluralistic Values in Large Language Model Alignment	Various (arXiv 2511.14476)	https://arxiv.org/pdf/2511.14476	2025-11-01	ai-alignment		paper	enrichment	high	pluralistic-alignment demographic-composition empirical safety-inclusivity real-human-feedback	theseus	2026-03-15	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	anthropic/claude-sonnet-4.5	modeling preference sensitivity as a learned distribution rather than a fixed scalar resolves DPO diversity failures without demographic labels or explicit user modeling the variance of a learned preference sensitivity distribution diagnoses dataset heterogeneity and collapses to fixed-parameter behavior when preferences are homogeneous

Content

Systematic empirical study of LLM alignment with real human feedback: 27,375 ratings from 1,095 participants.

Key Results (from search summary):

• Jointly varied demographic composition and technical design
• Models fine-tuned on Liberal, White, and Female feedback showed improvements of 5.0, 4.7, and 3.4 percentage points respectively
• Relative to Conservative, Black, and Male baselines
• Measured across emotional awareness and toxicity dimensions

Key Contribution: Demonstrates that "whose feedback" matters as much as "how much feedback" for alignment outcomes. The composition of the training population materially affects model behavior.

Agent Notes

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. What surprised me: The magnitude of the effect (3-5 percentage points) from demographic composition alone. This is not a subtle effect. 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. 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. Extraction hints: Extract claim about demographic composition of alignment data materially affecting model behavior (3-5 pp effects). Context: 1,095 participants is a large N for alignment research. Real human feedback, not synthetic.

Curator Notes (structured handoff for extractor)

PRIMARY CONNECTION: community-centred norm elicitation surfaces alignment targets materially different from developer-specified rules WHY ARCHIVED: Empirical evidence that "whose preferences" is a quantitatively important question, not just a fairness concern EXTRACTION HINT: Focus on the magnitude of demographic composition effects and what this means for single-population alignment training

Key Facts

• Study included 27,375 ratings from 1,095 participants
• Models fine-tuned on Liberal feedback showed 5.0 percentage point improvement over Conservative baseline
• Models fine-tuned on White feedback showed 4.7 percentage point improvement over Black baseline
• Models fine-tuned on Female feedback showed 3.4 percentage point improvement over Male baseline
• Effects measured across emotional awareness and toxicity dimensions