PopAlign: Diversifying Contrasting Patterns for a More Comprehensive Alignment

ACL ARR 2025 February Submission6863 Authors

Abstract: 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 \textbf{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 harmful response tendencies. 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 \textbf{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.

Paper Type: Long

Research Area: Dialogue and Interactive Systems

Research Area Keywords: Large Language Models, Alignment, Data Synthesis

Contribution Types: NLP engineering experiment

Languages Studied: English

Submission Number: 6863