  The advancement of large language models (LLMs) has enhanced the ability to
generalize across a wide range of unseen natural language processing (NLP)
tasks through instruction-following. Yet, their effectiveness often diminishes
in low-resource languages like Chinese, exacerbated by biased evaluations from
data leakage, casting doubt on their true generalizability to new linguistic
territories. In response, we introduce the Chinese Instruction-Following
Benchmark (CIF-Bench), designed to evaluate the zero-shot generalizability of
LLMs to the Chinese language. CIF-Bench comprises 150 tasks and 15,000
input-output pairs, developed by native speakers to test complex reasoning and
Chinese cultural nuances across 20 categories. To mitigate data contamination,
we release only half of the dataset publicly, with the remainder kept private,
and introduce diversified instructions to minimize score variance, totaling
45,000 data instances. Our evaluation of 28 selected LLMs reveals a noticeable
performance gap, with the best model scoring only 52.9%, highlighting the
limitations of LLMs in less familiar language and task contexts. This work not
only uncovers the current limitations of LLMs in handling Chinese language
tasks but also sets a new standard for future LLM generalizability research,
pushing towards the development of more adaptable, culturally informed, and
linguistically diverse models.
