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

Frederico Belcavello; Ely Matos; Arthur Lorenzi; Lisandra Bonoto; Lívia Ruiz; Luiz Fernando Pereira; Victor Herbst; Yulla Navarro; Helen de Andrade Abreu; Lívia Dutra; Tiago Timponi Torrent

arXiv · 2510.25904

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

Frederico Belcavello·Ely Matos·Arthur Lorenzi·Lisandra Bonoto·Lívia Ruiz·Luiz Fernando Pereira·Victor Herbst·Yulla Navarro·Helen de Andrade Abreu·Lívia Dutra·Tiago Timponi Torrent

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Abstract

The use of LLM-based applications as a means to accelerate and/or substitute human labor in the creation of language resources and dataset is a reality. Nonetheless, despite the potential of such tools for linguistic research, comprehensive 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 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.

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Frederico Belcavello, Ely Matos, Arthur Lorenzi, Lisandra Bonoto, Lívia Ruiz, Luiz Fernando Pereira, Victor Herbst, Yulla Navarro, Helen de Andrade Abreu, Lívia Dutra, Tiago Timponi Torrent. 2025-10-29. Evaluating the Impact of LLM-Assisted Annotation in a Perspectivized Setting: the Case of FrameNet Annotation. https://arxiv.org/abs/2510.25904

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