  Before being deployed for user-facing applications, developers align Large
Language Models (LLMs) to user preferences through a variety of procedures,
such as Reinforcement Learning From Human Feedback (RLHF) and Direct Preference
Optimization (DPO). Current evaluations of these procedures focus on benchmarks
of instruction following, reasoning, and truthfulness. However, human
preferences are not universal, and aligning to specific preference sets may
have unintended effects. We explore how alignment impacts performance along
three axes of global representation: English dialects, multilingualism, and
opinions from and about countries worldwide. Our results show that current
alignment procedures create disparities between English dialects and global
opinions. We find alignment improves capabilities in several languages. We
conclude by discussing design decisions that led to these unintended impacts
and recommendations for more equitable preference tuning. We make our code and
data publicly available on Github.
