mlsanityData to 14 Oct 2026

PopAlign: Diversifying Contrasting Patterns for a More Comprehensive Alignment

arXiv 2410.13785 ↗First posted 17 Oct 2024cs.CL cs.AIcited by 2

On the map: LLM alignment / RLHF / preference optimisation → LLM preference optimization methods

Abstract (arXiv)

Alignment of large language models (LLMs) involves training models on preference-contrastive output pairs to adjust their responses according to human preferences. To obtain such contrastive pairs, traditional methods like RLHF and RLAIF rely on limited contrasting patterns, such as varying model variants or decoding temperatures. This singularity leads to two issues: (1) alignment is not comprehensive; and thereby (2) models are susceptible to jailbreaking attacks. To address these issues, we investigate how to construct more comprehensive and diversified contrasting patterns to enhance preference data (RQ1) and verify the impact of the diversification of contrasting patterns on model alignment (RQ2). For RQ1, we propose PopAlign, a framework that integrates diversified contrasting patterns across the prompt, model, and pipeline levels, introducing six contrasting strategies that do not require additional feedback labeling procedures. Regarding RQ2, we conduct thorough experiments demonstrating that PopAlign significantly outperforms existing methods, leading to more comprehensive alignment.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in LLM preference optimization methods, one of 10 directions in LLM alignment / RLHF / preference optimisation. Builds on 25 of its 46 references that are in the map. First posted on arXiv 17 Oct 2024.

Builds on

25 of 46 references.

Closest ancestors · 4

Same direction, newest first

  1. 2023RLCD: Reinforcement Learning from Contrastive Distillation for Language Model Alignment24 Jul
  2. 2023Aligning Large Language Models through Synthetic Feedback23 May
  3. 2022Training language models to follow instructions with human feedback4 Mar
  4. 2021A General Language Assistant as a Laboratory for Alignment1 Dec

Other references · 21

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2017Proximal Policy Optimization Algorithmscited by 15,215
  3. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
  4. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,626
  5. 2023Direct Preference Optimization: Your Language Model is Secretly a Reward Modelcited by 8,065
  6. 2020Scaling Laws for Neural Language Modelscited by 6,586
  7. 2022Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedbackcited by 3,720
  8. 2023Self-Refine: Iterative Refinement with Self-Feedbackcited by 3,442
  9. 2023FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioningcited by 2,598
  10. 2024Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preferencecited by 1,184
  11. 2024Length-Controlled AlpacaEval: A Simple Way to Debias Automatic Evaluatorscited by 897
  12. 2023Enhancing Chat Language Models by Scaling High-quality Instructional Conversationscited by 839
9 more
  1. 2023AlpacaFarm: A Simulation Framework for Methods that Learn from Human Feedbackcited by 748
  2. 2023ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debatecited by 742
  3. 2022A Survey on In-context Learningcited by 721
  4. 2024Yi: Open Foundation Models by 01.AIcited by 719
  5. 2022Large Language Models Can Self-Improvecited by 664
  6. 2023Jailbreaking ChatGPT via Prompt Engineering: An Empirical Studycited by 498
  7. 2024Data Mixing Laws: Optimizing Data Mixtures by Predicting Language Modeling Performancecited by 184
  8. 2023ZeRO++: Extremely Efficient Collective Communication for Giant Model Trainingcited by 52
  9. 2023The ART of LLM Refinement: Ask, Refine, and Trustcited by 29

Built on by

Cited by year

Citing papers per year: 2025: 2.

2 papers.

  1. 2025A Survey on Personalized and Pluralistic Preference Alignment in Large Language Modelscited by 27
  2. 2025Recontextualization Mitigates Specification Gaming without Modifying the Specificationcited by 14

Similar, not cited

Close in wording, no citation either way

  1. 2024MetaAlign: Align Large Language Models with Diverse Preferences during Inference Time
  2. 2023On Diversified Preferences of Large Language Model Alignment
  3. 2026Preference Data Selection for Mitigating the Alignment Tax in Large Language Models
  4. 2024A Deep Dive into the Trade-Offs of Parameter-Efficient Preference Alignment Techniques
  5. 2025ComPO: Preference Alignment via Comparison Oracles
  6. 2025Data Selection for LLM Alignment Using Fine-Grained Preferences
  7. 2024Anchored Preference Optimization and Contrastive Revisions: Addressing Underspecification in Alignment
  8. 2025Towards Understanding Valuable Preference Data for Large Language Model Alignment