MEGAnno+: A Human-LLM Collaborative Annotation System
February 28, 2024 ยท Declared Dead ยท ๐ Conference of the European Chapter of the Association for Computational Linguistics
"No code URL or promise found in abstract"
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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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