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1.7 KiB
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23 lines
No EOL
1.7 KiB
Markdown
---
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type: claim
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title: Ideal point models from political science provide formal foundation for pluralistic preference modeling
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confidence: experimental
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domains: [ai-alignment, collective-intelligence]
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created: 2025-01-21
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---
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The PAL (Pluralistic Alignment via Learning) system adapts ideal point models from political science (Coombs, 1950) to AI alignment, representing each user's preferences as a position in latent space and modeling preference strength as distance from learned prototypes. This provides a formal mathematical framework for pluralistic alignment that achieves 36% improvement on unseen users compared to standard RLHF while using 100× fewer parameters than user-specific models.
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The architecture uses two components: Model A maps prompts to K learned prototypes in latent space, while Model B maps user identifiers to ideal points in the same space, with preference probability modeled as exp(-||prototype - ideal_point||²). This achieves sample complexity Õ(K) in the number of prototypes rather than Õ(D) in the number of users, enabling efficient generalization.
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## Relevant Notes
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- [[mixture-modeling-enables-sample-efficient-pluralistic-alignment-through-shared-prototype-structure]] - describes the K-prototype architecture in detail
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- [[universal-alignment-is-mathematically-impossible-because-arrows-impossibility-theorem-applies-to-aggregating-diverse-human-preferences-into-a-single-coherent-objective]] - the impossibility result that motivates pluralistic approaches
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- [[Collective intelligence]] - wiki context on aggregating diverse perspectives
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- [[Political science]] - source domain for ideal point models
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## Source
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PAL: Pluralistic Alignment via Learning (ICLR 2025)
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Extracted: 2025-01-21 by Theseus |