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

TL;DR

This study reveals that a significant portion of crowd workers on Amazon Mechanical Turk use large language models to complete text summarization tasks, raising concerns about data authenticity and the impact on research quality.

Contribution

The paper introduces a methodology combining keystroke detection and synthetic text classification to estimate LLM usage among crowd workers, highlighting the need for new safeguards.

Findings

33-46% of crowd workers used LLMs for task completion

LLM usage impacts the validity of human-generated annotations

Proposes a method to detect LLM usage in crowdsourcing tasks

Abstract

Large language models (LLMs) are remarkable data annotators. They can be used to generate high-fidelity supervised training data, as well as survey and experimental data. With the widespread adoption of LLMs, human gold--standard annotations are key to understanding the capabilities of LLMs and the validity of their results. However, crowdsourcing, an important, inexpensive way to obtain human annotations, may itself be impacted by LLMs, as crowd workers have financial incentives to use LLMs to increase their productivity and income. To investigate this concern, we conducted a case study on the prevalence of LLM usage by crowd workers. We reran an abstract summarization task from the literature on Amazon Mechanical Turk and, through a combination of keystroke detection and synthetic text classification, estimate that 33-46% of crowd workers 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 data remain human, perhaps using the methodology proposed here as a stepping stone. Code/data: https://github.com/epfl-dlab/GPTurk