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Evaluating the Impact of LLM-Assisted Annotation in a Perspectivized Setting: The Case of FrameNet Annotation

The use of LLM-based applications as a means to accelerate and/or substitute human labor in the creation of language resources and datasets is a reality. Nonetheless, despite the potential of such tools for linguistic research, an evaluation of their performance and impact on the creation of annotated datasets, especially under a perspectivized approach to NLP, is still missing. This paper contributes to the reduction of this gap by reporting on an extensive evaluation of the (semi-)automatization of FrameNet-like semantic annotation by the use of an LLM-based semantic role labeler. The methodology employed compares annotation time, coverage, and diversity in three experimental settings: manual, automatic, and semi-automatic annotation. Results show that the hybrid, semi-automatic annotation setting leads to increased frame diversity and similar annotation coverage, when compared to the human-only setting, while the automatic setting performs considerably worse in all metrics, except for annotation time, which remains similar.