MEGAnno+: A Human-LLM Collaborative Annotation System

February 28, 2024 ยท Declared Dead ยท ๐Ÿ› Conference of the European Chapter of the Association for Computational Linguistics

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Authors Hannah Kim, Kushan Mitra, Rafael Li Chen, Sajjadur Rahman, Dan Zhang arXiv ID 2402.18050 Category cs.CL: Computation & Language Cross-listed cs.HC Citations 55 Venue Conference of the European Chapter of the Association for Computational Linguistics Last Checked 3 months ago
Abstract
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.
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