Recursive generative retraining with pluralistic preferences converges to a stable diverse distribution that satisfies a weighted Nash bargaining solution.
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A roadmap to pluralistic alignment
13 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 13representative citing papers
A technique identifies minimal convergence-divergence points in LLM transformer blocks and calibrates residual-stream directions to achieve targeted ethical-framework control at inference time.
RLHF should decompose annotations into dimensions each matched to one of three models—extension, evidence, or authority—instead of applying a single unified pipeline.
DISCA uses disagreement among WVS-grounded persona panels to apply loss-averse logit corrections that reduce cultural misalignment by 10-24% on MultiTP for models 3.8B and larger, without weight changes.
Annotator Policy Models learn safety policies from labeling behavior alone, accurately predicting responses and revealing sources of disagreement like policy ambiguity and value pluralism.
MSD enables cross-lingual safety transfer in LLMs via self-distillation with Dual-Perspective Safety Weighting, improving safety in low-resource languages without target response data.
Personalized RewardBench reveals that state-of-the-art reward models reach only 75.94% accuracy on personalized preferences and shows stronger correlation with downstream BoN and PPO performance than prior benchmarks.
LLMs display Western-centric cultural representations that align poorly with native priorities in non-Western countries and share highly correlated error patterns.
Community members from the UK blind community, Kerala, and Tamil Nadu helped define what counts as culturally appropriate depictions of artifacts, and the authors tested whether those definitions can be turned into repeatable LLM-as-a-judge measurements.
A tradeoff model shows generative AI can reduce bias against diverse preferences by strategically eliciting information instead of always inferring from majority patterns.
Annotation disagreement on toxic language can be moderately predicted from textual features, with high-opposition items proving harder for models to estimate accurately.
AI value alignment is reconceptualized as a pluralistic governance problem arising along three axes—objectives, information, and principals—making it inherently context-dependent and unsolvable by technical design alone.
citing papers explorer
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Curated Synthetic Data Doesn't Have to Collapse: A Theoretical Study of Generative Retraining with Pluralistic Preferences
Recursive generative retraining with pluralistic preferences converges to a stable diverse distribution that satisfies a weighted Nash bargaining solution.
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Where Paths Split: Localized, Calibrated Control of Moral Reasoning in Large Language Models
A technique identifies minimal convergence-divergence points in LLM transformer blocks and calibrates residual-stream directions to achieve targeted ethical-framework control at inference time.
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Three Models of RLHF Annotation: Extension, Evidence, and Authority
RLHF should decompose annotations into dimensions each matched to one of three models—extension, evidence, or authority—instead of applying a single unified pipeline.
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Training-Free Cultural Alignment of Large Language Models via Persona Disagreement
DISCA uses disagreement among WVS-grounded persona panels to apply loss-averse logit corrections that reduce cultural misalignment by 10-24% on MultiTP for models 3.8B and larger, without weight changes.
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Understanding Annotator Safety Policy with Interpretability
Annotator Policy Models learn safety policies from labeling behavior alone, accurately predicting responses and revealing sources of disagreement like policy ambiguity and value pluralism.
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Multilingual Safety Alignment via Self-Distillation
MSD enables cross-lingual safety transfer in LLMs via self-distillation with Dual-Perspective Safety Weighting, improving safety in low-resource languages without target response data.
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Personalized RewardBench: Evaluating Reward Models with Human Aligned Personalization
Personalized RewardBench reveals that state-of-the-art reward models reach only 75.94% accuracy on personalized preferences and shows stronger correlation with downstream BoN and PPO performance than prior benchmarks.
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Cultural Authenticity: Comparing LLM Cultural Representations to Native Human Expectations
LLMs display Western-centric cultural representations that align poorly with native priorities in non-Western countries and share highly correlated error patterns.
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Evaluating AI-Generated Images of Cultural Artifacts with Community-Informed Rubrics
Community members from the UK blind community, Kerala, and Tamil Nadu helped define what counts as culturally appropriate depictions of artifacts, and the authors tested whether those definitions can be turned into repeatable LLM-as-a-judge measurements.
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When to Ask a Question: Understanding Communication Strategies in Generative AI Tools
A tradeoff model shows generative AI can reduce bias against diverse preferences by strategically eliciting information instead of always inferring from majority patterns.
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Quantifying and Predicting Disagreement in Graded Human Ratings
Annotation disagreement on toxic language can be moderately predicted from textual features, with high-opposition items proving harder for models to estimate accurately.
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Relative Principals, Pluralistic Alignment, and the Structural Value Alignment Problem
AI value alignment is reconceptualized as a pluralistic governance problem arising along three axes—objectives, information, and principals—making it inherently context-dependent and unsolvable by technical design alone.
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