Large Language Models as Span Annotators

• URL: http://arxiv.org/abs/2504.08697v2
• Date: Tue, 24 Jun 2025 13:11:18 GMT
• Title: Large Language Models as Span Annotators
• Authors: Zdeněk Kasner, Vilém Zouhar, Patrícia Schmidtová, Ivan Kartáč, Kristýna Onderková, Ondřej Plátek, Dimitra Gkatzia, Saad Mahamood, Ondřej Dušek, Simone Balloccu,
• Abstract summary: We show that large language models (LLMs) can serve as flexible and cost-effective span annotation backbones.<n>We demonstrate that LLMs achieve inter-annotator agreement (IAA) comparable to human annotators at a fraction of a cost per output annotation.
• Score: 5.488183187190419
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Span annotation is the task of localizing and classifying text spans according to custom guidelines. Annotated spans can be used to analyze and evaluate high-quality texts for which single-score metrics fail to provide actionable feedback. Until recently, span annotation was limited to human annotators or fine-tuned models. In this study, we show that large language models (LLMs) can serve as flexible and cost-effective span annotation backbones. To demonstrate their utility, we compare LLMs to skilled human annotators on three diverse span annotation tasks: evaluating data-to-text generation, identifying translation errors, and detecting propaganda techniques. We demonstrate that LLMs achieve inter-annotator agreement (IAA) comparable to human annotators at a fraction of a cost per output annotation. We also manually analyze model outputs, finding that LLMs make errors at a similar rate to human annotators. We release the dataset of more than 40k model and human annotations for further research.

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