  The recent advances in natural language processing (NLP), have led to a new
trend of applying large language models (LLMs) to real-world scenarios. While
the latest LLMs are astonishingly fluent when interacting with humans, they
suffer from the misinformation problem by unintentionally generating factually
false statements. This can lead to harmful consequences, especially when
produced within sensitive contexts, such as healthcare. Yet few previous works
have focused on evaluating misinformation in the long-form (LF) generation of
LLMs, especially for knowledge-intensive topics. Moreover, although LLMs have
been shown to perform well in different languages, misinformation evaluation
has been mostly conducted in English. To this end, we present a benchmark,
CARE-MI, for evaluating LLM misinformation in: 1) a sensitive topic,
specifically the maternity and infant care domain; and 2) a language other than
English, namely Chinese. Most importantly, we provide an innovative paradigm
for building LF generation evaluation benchmarks that can be transferred to
other knowledge-intensive domains and low-resourced languages. Our proposed
benchmark fills the gap between the extensive usage of LLMs and the lack of
datasets for assessing the misinformation generated by these models. It
contains 1,612 expert-checked questions, accompanied with human-selected
references. Using our benchmark, we conduct extensive experiments and found
that current Chinese LLMs are far from perfect in the topic of maternity and
infant care. In an effort to minimize the reliance on human resources for
performance evaluation, we offer off-the-shelf judgment models for
automatically assessing the LF output of LLMs given benchmark questions.
Moreover, we compare potential solutions for LF generation evaluation and
provide insights for building better automated metrics.
