PopAlign: Diversifying Contrasting Patterns for a More Comprehensive Alignment
Zekun Moore Wang, Shawn Wang, Kang Zhu, Jiaheng Liu, Ke Xu, Jie Fu, Wangchunshu Zhou, Wenhao Huang
2024-10-18
Summary
This paper introduces PopAlign, a new method for improving how large language models (LLMs) learn to align their responses with human preferences by using diverse contrasting patterns.
What's the problem?
Current methods for aligning LLMs often rely on limited ways of contrasting different outputs, which can lead to two main issues: the alignment is not thorough enough, and the models are vulnerable to manipulative attacks (known as jailbreaking). This means that the models might not respond accurately or could be tricked into giving wrong answers.
What's the solution?
To solve these problems, the authors developed PopAlign, a framework that creates a variety of contrasting patterns across different levels, such as prompts and models. They introduced six different strategies for contrasting responses, which help the model learn better without needing extra feedback. By using these diverse strategies, PopAlign enhances the model's ability to understand and align with human preferences more effectively. The authors conducted experiments showing that PopAlign significantly outperforms previous methods in producing accurate and reliable responses.
Why it matters?
This research is important because it helps improve the reliability and safety of AI systems that rely on language models. By ensuring these models are better aligned with human preferences, PopAlign can lead to more trustworthy AI applications in areas like customer service, healthcare, and education, where accurate information is crucial.
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.