Group Preference Optimization: Few-Shot Alignment of Large Language Models

Understand

Many applications of large language models (LLMs), ranging from chatbots to creative writing, require nuanced subjective judgments that can differ significantly across different groups.

• Existing alignment algorithms can be expensive to align for each group, requiring prohibitive amounts of group-specific preference data and computation for real-world use cases.
• We introduce Group Preference Optimization (GPO), an alignment framework that steers language models to preferences of individual groups in a few-shot manner.
• In GPO, we augment the base LLM with an independent transformer module trained to predict the preferences of a group for the LLM generations.