Are Expert-Level Language Models Expert-Level Annotators?

Fuente: arXiv
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Main Authors: Tseng, Yu-Min, Chen, Wei-Lin, Chen, Chung-Chi, Chen, Hsin-Hsi
Format: Preprint
Published: 2024
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author Tseng, Yu-Min
Chen, Wei-Lin
Chen, Chung-Chi
Chen, Hsin-Hsi
author_facet Tseng, Yu-Min
Chen, Wei-Lin
Chen, Chung-Chi
Chen, Hsin-Hsi
contents Data annotation refers to the labeling or tagging of textual data with relevant information. A large body of works have reported positive results on leveraging LLMs as an alternative to human annotators. However, existing studies focus on classic NLP tasks, and the extent to which LLMs as data annotators perform in domains requiring expert knowledge remains underexplored. In this work, we investigate comprehensive approaches across three highly specialized domains and discuss practical suggestions from a cost-effectiveness perspective. To the best of our knowledge, we present the first systematic evaluation of LLMs as expert-level data annotators.
format Preprint
id arxiv_https___arxiv_org_abs_2410_03254
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Are Expert-Level Language Models Expert-Level Annotators?
Tseng, Yu-Min
Chen, Wei-Lin
Chen, Chung-Chi
Chen, Hsin-Hsi
Computation and Language
Data annotation refers to the labeling or tagging of textual data with relevant information. A large body of works have reported positive results on leveraging LLMs as an alternative to human annotators. However, existing studies focus on classic NLP tasks, and the extent to which LLMs as data annotators perform in domains requiring expert knowledge remains underexplored. In this work, we investigate comprehensive approaches across three highly specialized domains and discuss practical suggestions from a cost-effectiveness perspective. To the best of our knowledge, we present the first systematic evaluation of LLMs as expert-level data annotators.
title Are Expert-Level Language Models Expert-Level Annotators?
topic Computation and Language
url https://arxiv.org/abs/2410.03254