mlsanityData to 7 Oct 2026

Learning a Canonical Basis of Human Preferences from Binary Ratings

arXiv 2503.24150 ↗First posted 31 Mar 2025cs.LG cs.AI cs.HCcited by 0

On the map: LLM alignment / RLHF / preference optimisation → Reward modeling and RLHF

Abstract (arXiv)

Recent advances in generative AI have been driven by alignment techniques such as reinforcement learning from human feedback (RLHF). RLHF and related techniques typically involve constructing a dataset of binary or ranked choice human preferences and subsequently fine-tuning models to align with these preferences. This paper shifts the focus to understanding the preferences encoded in such datasets and identifying common human preferences. We find that a small subset of 21 preference categories (selected from a set of nearly 5,000 distinct preferences) captures >89% of preference variation across individuals. This small set of preferences is analogous to a canonical basis of human preferences, similar to established findings that characterize human variation in psychology or facial recognition studies. Through both synthetic and empirical evaluations, we confirm that our low-rank, canonical set of human preferences generalizes across the entire dataset and within specific topics. We further demonstrate our preference basis' utility in model evaluation, where our preference categories offer deeper insights into model alignment, and in model training, where we show that fine-tuning on preference-defined subsets successfully aligns the model accordingly.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Reward modeling and RLHF, one of 10 directions in LLM alignment / RLHF / preference optimisation. Builds on 20 of its 31 references that are in the map. First posted on arXiv 31 Mar 2025.

Builds on

20 of 31 references.

Closest ancestors · 5

Same direction, newest first

  1. 2024Personalizing Reinforcement Learning from Human Feedback with Variational Preference Learning19 Aug
  2. 2024Interpretable Preferences via Multi-Objective Reward Modeling and Mixture-of-Experts18 Jun
  3. 2023A Long Way to Go: Investigating Length Correlations in RLHF5 Oct
  4. 2023Direct Preference Optimization: Your Language Model is Secretly a Reward Model29 May
  5. 2017Deep reinforcement learning from human preferences12 Jun

Other references · 15

  1. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,393
  2. 2024GPT-4o System Cardcited by 4,467
  3. 2022Constitutional AI: Harmlessness from AI Feedbackcited by 2,950
  4. 2024Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preferencecited by 1,151
  5. 2023A General Theoretical Paradigm to Understand Learning from Human Preferencescited by 906
  6. 2023LaMP: When Large Language Models Meet Personalizationcited by 425
  7. 2021Understanding Dataset Difficulty with V-Usable Informationcited by 288
  8. 2024A Mechanistic Understanding of Alignment Algorithms: A Case Study on DPO and Toxicitycited by 179
  9. 2024Rewards-in-Context: Multi-objective Alignment of Foundation Models with Dynamic Preference Adjustmentcited by 171
  10. 2023HelpSteer: Multi-attribute Helpfulness Dataset for SteerLMcited by 140
  11. 2024Direct Preference Optimization with an Offsetcited by 122
  12. 2024Democratizing Large Language Models via Personalized Parameter-Efficient Fine-tuningcited by 87
3 more
  1. 2023Image Clustering Conditioned on Text Criteriacited by 26
  2. 2024Text Clustering as Classification with LLMscited by 16
  3. 2025Beyond QA Pairs: Assessing Parameter-Efficient Fine-Tuning for Fact Embedding in LLMscited by 1

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Close in wording, no citation either way

  1. 2024Data-Centric Human Preference with Rationales for Direct Preference Alignment
  2. 2025Influencing Humans to Conform to Preference Models for RLHF
  3. 2024Preference Tuning with Human Feedback on Language, Speech, and Vision Tasks: A Survey
  4. 2025Rethinking Diverse Human Preference Learning through Principal Component Analysis
  5. 2024LRHP: Learning Representations for Human Preferences via Preference Pairs
  6. 2026Are Human-Aligned Models Models of Humans? A Turing-Test Gap in Preference Alignment
  7. 2024PAL: Pluralistic Alignment Framework for Learning from Heterogeneous Preferences
  8. 2025Skywork-Reward-V2: Scaling Preference Data Curation via Human-AI Synergy