Evaluating how LLM annotations represent diverse views on contentious topics
Journal:
arXiv
Published Date:
Mar 29, 2025
Abstract
Researchers have proposed the use of generative large language models (LLMs)
to label data for both research and applied settings. This literature
emphasizes the improved performance of LLMs relative to other natural language
models, noting that LLMs typically outperform other models on standard metrics
such as accuracy, precision, recall, and F1 score. However, previous literature
has also highlighted the bias embedded in language models, particularly around
contentious topics such as potentially toxic content. This bias could result in
labels applied by LLMs that disproportionately align with majority groups over
a more diverse set of viewpoints. In this paper, we evaluate how LLMs represent
diverse viewpoints on these contentious tasks. Across four annotation tasks on
four datasets, we show that LLMs do not show substantial disagreement with
annotators on the basis of demographics. Instead, the model, prompt, and
disagreement between human annotators on the labeling task are far more
predictive of LLM agreement. Our findings suggest that when using LLMs to
annotate data, under-representing the views of particular groups is not a
substantial concern. We conclude with a discussion of the implications for
researchers and practitioners.
