Bias In Llms As Annotators: The Effect Of Party Cues On Labelling Decision By Large Language Models

Abstract

Human coders are biased. We test similar biases in Large Language Models (LLMs) as annotators. By replicating an experiment run by Ennser-Jedenastik and Meyer (2018), we find evidence that LLMs use political information, and specifically party cues, to judge political statements. Not only do LLMs use relevant information to contextualize whether a statement is positive, negative, or neutral based