If in a Crowdsourced Data Annotation Pipeline, a GPT-4

Zeyu He, Chieh Yang Huang, Chien Kuang Cornelia Ding, Shaurya Rohatgi, Ting Hao Huang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Recent studies indicated GPT-4 outperforms online crowd workers in data labeling accuracy, notably workers from Amazon Mechanical Turk (MTurk). However, these studies were criticized for deviating from standard crowdsourcing practices and emphasizing individual workers' performances over the whole data-annotation process. This paper compared GPT-4 and an ethical and well-executed MTurk pipeline, with 415 workers labeling 3,177 sentence segments from 200 scholarly articles using the CODA-19 scheme. Two worker interfaces yielded 127,080 labels, which were then used to infer the final labels through eight label-aggregation algorithms. Our evaluation showed that despite best practices, MTurk pipeline's highest accuracy was 81.5%, whereas GPT-4 achieved 83.6%. Interestingly, when combining GPT-4's labels with crowd labels collected via an advanced worker interface for aggregation, 2 out of the 8 algorithms achieved an even higher accuracy (87.5%, 87.0%). Further analysis suggested that, when the crowd's and GPT-4's labeling strengths are complementary, aggregating them could increase labeling accuracy.

Original languageEnglish (US)
Title of host publicationCHI 2024 - Proceedings of the 2024 CHI Conference on Human Factors in Computing Sytems
PublisherAssociation for Computing Machinery
ISBN (Electronic)9798400703300
DOIs
StatePublished - May 11 2024
Event2024 CHI Conference on Human Factors in Computing Sytems, CHI 2024 - Hybrid, Honolulu, United States
Duration: May 11 2024May 16 2024

Publication series

NameConference on Human Factors in Computing Systems - Proceedings

Conference

Conference2024 CHI Conference on Human Factors in Computing Sytems, CHI 2024
Country/TerritoryUnited States
CityHybrid, Honolulu
Period5/11/245/16/24

All Science Journal Classification (ASJC) codes

  • Software
  • Human-Computer Interaction
  • Computer Graphics and Computer-Aided Design

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