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

arXiv:2402.16795v1 Announce Type: cross
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 …

abstract accuracy amazon annotation arxiv crowdsourcing cs.ai cs.cl cs.hc cs.lg data data annotation data labeling gpt gpt-4 labeling mechanical turk paper performances pipeline practices process standard studies type workers