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.

翻译：大型语言模型（LLMs）能以比人类更快、更低的成本完成各种自然语言处理任务的数据标注。尽管性能强大，LLMs在理解复杂、社会文化或特定领域上下文方面仍存在不足，可能导致错误标注。因此，我们倡导一种人类与LLM协作的方法，共同生成可靠且高质量的标签。我们提出MEGAnno+系统，这是一种人类与LLM协作的标注系统，提供高效的LLM智能体与标注管理、便捷稳健的LLM标注，以及人类对LLM标签的探索性验证功能。