@inproceedings{kim-etal-2024-meganno,
title = "{MEGA}nno+: A Human-{LLM} Collaborative Annotation System",
author = "Kim, Hannah and
Mitra, Kushan and
Li Chen, Rafael and
Rahman, Sajjadur and
Zhang, Dan",
editor = "Aletras, Nikolaos and
De Clercq, Orphee",
booktitle = "Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations",
month = mar,
year = "2024",
address = "St. Julians, Malta",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.eacl-demo.18",
pages = "168--176",
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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<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.</abstract>
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%0 Conference Proceedings
%T MEGAnno+: A Human-LLM Collaborative Annotation System
%A Kim, Hannah
%A Mitra, Kushan
%A Li Chen, Rafael
%A Rahman, Sajjadur
%A Zhang, Dan
%Y Aletras, Nikolaos
%Y De Clercq, Orphee
%S Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations
%D 2024
%8 March
%I Association for Computational Linguistics
%C St. Julians, Malta
%F kim-etal-2024-meganno
%X 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.
%U https://aclanthology.org/2024.eacl-demo.18
%P 168-176
Markdown (Informal)
[MEGAnno+: A Human-LLM Collaborative Annotation System](https://aclanthology.org/2024.eacl-demo.18) (Kim et al., EACL 2024)
ACL
- Hannah Kim, Kushan Mitra, Rafael Li Chen, Sajjadur Rahman, and Dan Zhang. 2024. MEGAnno+: A Human-LLM Collaborative Annotation System. In Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations, pages 168–176, St. Julians, Malta. Association for Computational Linguistics.