PopAlign: Diversifying Contrasting Patterns for a More Comprehensive Alignment

TL;DR

PopAlign introduces diversified contrasting patterns at multiple levels to improve large language model alignment, addressing limitations of traditional methods and enhancing robustness against jailbreaking.

Contribution

It proposes a novel framework, PopAlign, that diversifies contrasting patterns without extra feedback, significantly improving alignment comprehensiveness and robustness.

Findings

PopAlign outperforms existing methods in alignment quality.

Diversified contrasting patterns enhance model robustness.

Framework does not require additional feedback labeling.

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 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.