  We present a new challenge to examine whether large language models
understand social norms. In contrast to existing datasets, our dataset requires
a fundamental understanding of social norms to solve. Our dataset features the
largest set of social norm skills, consisting of 402 skills and 12,383
questions covering a wide set of social norms ranging from opinions and
arguments to culture and laws. We design our dataset according to the K-12
curriculum. This enables the direct comparison of the social understanding of
large language models to humans, more specifically, elementary students. While
prior work generates nearly random accuracy on our benchmark, recent large
language models such as GPT3.5-Turbo and LLaMA2-Chat are able to improve the
performance significantly, only slightly below human performance. We then
propose a multi-agent framework based on large language models to improve the
models' ability to understand social norms. This method further improves large
language models to be on par with humans. Given the increasing adoption of
large language models in real-world applications, our finding is particularly
important and presents a unique direction for future improvements.
