Large Language Model Hacking: Quantifying the Hidden Risks of Using LLMs for Text Annotation
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Resumen del artículo
This study finds a substantial risk of drawing incorrect conclusions in social science research when using Large Language Models (LLMs) for text annotation, with an average of one in three hypotheses leading to false conclusions due to variations in LLM configuration ('LLM hacking'). Even highly accurate LLMs are susceptible, and intentional manipulation to achieve desired outcomes is alarmingly easy.
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Using AI to label data for research can lead to wrong answers, like getting a bad grade on a test because the teacher used a faulty grading system. Even good AI can mess up, so we need to double-check its work.
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Explicación de la calificación
This paper reveals a critical, previously overlooked issue in computational social science and quantifies the risks associated with using LLMs for data annotation. The methodology is rigorous, involving a large-scale replication study across diverse tasks and models. While there are limitations regarding the ground truth assumption and the explored configuration space, the findings are substantial and have significant implications for research practice. The paper also offers practical recommendations to mitigate the identified risks, which enhances its value to the scientific community.
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