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Large language models (LLMs) are remarkable data annotators. They can be usedto generate high-fidelity supervised training data, as well as survey andexperimental data. With the widespread adoption of LLMs, human gold--standardannotations are key to understanding the capabilities of LLMs and the validityof their results. However, crowdsourcing, an important, inexpensive way toobtain human annotations, may itself be impacted by LLMs, as crowd workers havefinancial incentives to use LLMs to increase their productivity and income. Toinvestigate this concern, we conducted a case study on the prevalence of LLMusage by crowd workers. We reran an abstract summarization task from theliterature on Amazon Mechanical Turk and, through a combination of keystrokedetection and synthetic text classification, estimate that 33-46% of crowdworkers used LLMs when completing the task. Although generalization to other,less LLM-friendly tasks is unclear, our results call for platforms,researchers, and crowd workers to find new ways to ensure that human dataremain human, perhaps using the methodology proposed here as a stepping stone.Code/data: https://github.com/epfl-dlab/GPTurk

Artificial Artificial Artificial Intelligence: Crowd Workers Widely Use Large Language Models for Text Production Tasks