  Large language models (LLMs) can label data faster and cheaper than humans
for various NLP tasks. Despite their prowess, LLMs may fall short in
understanding of complex, sociocultural, or domain-specific context,
potentially leading to incorrect annotations. Therefore, we advocate a
collaborative approach where humans and LLMs work together to produce reliable
and high-quality labels. We present MEGAnno+, a human-LLM collaborative
annotation system that offers effective LLM agent and annotation management,
convenient and robust LLM annotation, and exploratory verification of LLM
labels by humans.
