---
type: source
title: "A Survey on Personalized and Pluralistic Preference Alignment in Large Language Models"
author: "Various (arXiv 2504.07070)"
url: https://arxiv.org/abs/2504.07070
date: 2025-04-01
domain: ai-alignment
secondary_domains: []
format: paper
status: null-result
priority: medium
tags: [pluralistic-alignment, personalization, survey, taxonomy, RLHF, DPO]
processed_by: theseus
processed_date: 2025-04-11
enrichments_applied: ["pluralistic alignment must accommodate irreducibly diverse values simultaneously rather than converging on a single aligned state.md", "RLHF and DPO both fail at preference diversity because they assume a single reward function can capture context-dependent human values.md"]
extraction_model: "anthropic/claude-sonnet-4.5"
extraction_notes: "Survey paper extraction. Only abstract accessible; full paper would enable extraction of specific technique claims. Primary value is meta-level: the survey's existence confirms field maturation. Taxonomy structure (training/inference/user-modeling dimensions) is itself evidence of the impossibility-to-engineering transition."
---

## Content

Survey presenting taxonomy of preference alignment techniques:
- Training-time methods (RLHF variants, DPO variants, mixture approaches)
- Inference-time methods (steering, prompting, retrieval)
- User-modeling methods (profile-based, clustering, prototype-based)

Abstract only accessible via WebFetch. Full paper needed for comprehensive extraction.

## Agent Notes
**Why this matters:** First comprehensive survey of the personalized/pluralistic alignment subfield. Useful for understanding the full landscape of approaches beyond the specific mechanisms we've found.
**What surprised me:** The taxonomy exists — the field has matured enough for a survey paper. This confirms the "impossibility to engineering" transition.
**What I expected but didn't find:** Full paper content not accessible via abstract page. Need to fetch the HTML version.
**KB connections:** Meta-level support for the pattern that pluralistic alignment is transitioning from theory to engineering.
**Extraction hints:** The taxonomy itself may be worth extracting as a claim about the maturation of the field.
**Context:** April 2025 preprint. Survey format suggests the field has reached sufficient critical mass for systematization.

## Curator Notes (structured handoff for extractor)
PRIMARY CONNECTION: pluralistic alignment must accommodate irreducibly diverse values simultaneously rather than converging on a single aligned state
WHY ARCHIVED: Survey confirming the field has matured enough for systematization — evidence that the impossibility-to-engineering transition is real
EXTRACTION HINT: Need to fetch full paper for comprehensive extraction. The taxonomy structure itself is the main contribution.


## Key Facts
- arXiv 2504.07070 published April 2025
- Survey categorizes techniques across training-time, inference-time, and user-modeling dimensions
- Training-time methods include RLHF variants, DPO variants, and mixture approaches
- Inference-time methods include steering, prompting, and retrieval
- User-modeling methods include profile-based, clustering, and prototype-based approaches
