MEGAnno+: A Human-LLM Collaborative Annotation System
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916141341868032 |
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| author | Kim, Hannah Mitra, Kushan Chen, Rafael Li Rahman, Sajjadur Zhang, Dan |
| author_facet | Kim, Hannah Mitra, Kushan Chen, Rafael Li Rahman, Sajjadur Zhang, Dan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_18050 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MEGAnno+: A Human-LLM Collaborative Annotation System Kim, Hannah Mitra, Kushan Chen, Rafael Li Rahman, Sajjadur Zhang, Dan Computation and Language Human-Computer Interaction 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. |
| title | MEGAnno+: A Human-LLM Collaborative Annotation System |
| topic | Computation and Language Human-Computer Interaction |
| url | https://arxiv.org/abs/2402.18050 |