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This is a Quantitative Study study.

September 23, 2025Information Systems ResearchOpen Access

Quality Control for Crowd Workers and for Language Models: A Framework for Free-Text Response Evaluation with No Ground Truth

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AQER achieves state-of-the-art performance in evaluating free-text responses from crowd workers and LLMs.
It provides a cost-effective and scalable methodology that does not rely on preexisting correct answers.
The framework aggregates multiple responses intelligently, leveraging the wisdom of the crowd for reliability.
AQER ensures continuous monitoring of AI performance, helping mitigate financial and reputational risks.

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This framework evaluates free-text responses from crowd workers and language models, suggesting improvements for AI performance.

Yahav et al. (2025) studied this question.

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