  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. For
few-shot learning, we parameterize this module as an in-context autoregressive
transformer and train it via meta-learning on several groups. We empirically
validate the efficacy of GPO through rigorous evaluations using LLMs with
varied sizes on three human opinion adaptation tasks. These tasks involve
adapting to the preferences of US demographic groups, global countries, and
individual users. Our results demonstrate that GPO not only aligns models more
accurately but also requires fewer group-specific preferences, and less
training and inference computing resources, outperforming existing strategies
such as in-context steering and fine-tuning methods.
