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.
@article{arxiv.2402.18050,
title = {MEGAnno+: A Human-LLM Collaborative Annotation System},
author = {Hannah Kim and Kushan Mitra and Rafael Li Chen and Sajjadur Rahman and Dan Zhang},
journal= {arXiv preprint arXiv:2402.18050},
year = {2024}
}