2026

1. Fine-Grained Perspectives: Modeling Explanations through Annotator-Specific Rationales

   Olufunke O. Sarumi, Charles Welch, and Daniel Braun

   In Proceedings of the The 5th Workshop on Perspectivist Approaches to NLP. [to be published], 2026

   Bib

   @inproceedings{sarumi-etal-2026-fine,
     title = {Fine-Grained Perspectives: Modeling Explanations through Annotator-Specific Rationales},
     author = {Sarumi, Olufunke O. and Welch, Charles and Braun, Daniel},
     booktitle = {Proceedings of the The 5th Workshop on Perspectivist Approaches to NLP},
     year = {2026},
     publisher = {[to be published]}
   }

2025

1. NLP-ResTeam at LeWiDi-2025:Performance Shifts in Perspective Aware Models based on Evaluation Metrics

   Olufunke O. Sarumi, Charles Welch, and Daniel Braun

   In Proceedings of the The 4th Workshop on Perspectivist Approaches to NLP. Association for Computational Linguistics, 2025

   DOI Link Abs Bib PDF

   Recent works in Natural Language Processing have focused on developing methods to model annotator perspectives within subjective datasets, aiming to capture opinion diversity. This has led to the development of various approaches that learn from disaggregated labels, leading to the question of what factors most influence the performance of these models. While dataset characteristics are a critical factor, the choice of evaluation metric is equally crucial, especially given the fluid and evolving concept of perspectivism. A model considered state-of-the-art under one evaluation scheme may not maintain its top-tier status when assessed with a different set of metrics, highlighting a potential challenge between model performance and the evaluation framework. This paper presents a performance analysis of annotator modeling approaches using the evaluation metrics of the 2025 Learning With Disagreement (LeWiDi) shared task and additional metrics. We evaluate five annotator-aware models under the same configurations. Our findings demonstrate a significant metric-induced shift in model rankings. Across four datasets, no single annotator modeling approach consistently outperformed others using a single metric, revealing that the “best” model is highly dependent on the chosen evaluation metric. This study systematically shows that evaluation metrics are not agnostic in the context of perspectivist model assessment.

   @inproceedings{sarumi-etal-2025-nlp,
     title = {{NLP}-{R}es{T}eam at {L}e{W}i{D}i-2025:Performance Shifts in Perspective Aware Models based on Evaluation Metrics},
     author = {Sarumi, Olufunke O. and Welch, Charles and Braun, Daniel},
     editor = {Abercrombie, Gavin and Basile, Valerio and Frenda, Simona and Tonelli, Sara and Dudy, Shiran},
     booktitle = {Proceedings of the The 4th Workshop on Perspectivist Approaches to NLP},
     month = nov,
     year = {2025},
     address = {Suzhou, China},
     publisher = {Association for Computational Linguistics},
     url = {https://aclanthology.org/2025.nlperspectives-1.19/},
     doi = {10.18653/v1/2025.nlperspectives-1.19},
     pages = {219--227},
     isbn = {979-8-89176-350-0},
   }

2. The Impact of Annotator Personas on LLM Behavior Across the Perspectivism Spectrum

   Olufunke O. Sarumi, Charles Welch, Daniel Braun, and 1 more author

   In Proceedings of the 8th International Conference on Natural Language and Speech Processing (ICNLSP-2025). Association for Computational Linguistics, 2025

   DOI Link Abs Bib PDF

   In this work, we explore the capability of Large Language Models (LLMs) to annotate hate speech and abusiveness while considering predefined annotator personas within the strong-to-weak data perspectivism spectra. We evaluated LLM-generated annotations against existing annotator modeling techniques for perspective modeling. Our findings show that LLMs selectively use demographic attributes from the personas. We identified prototypical annotators, with persona features that show varying degrees of alignment with the original human annotators. Within the data perspectivism paradigm, annotator modeling techniques that do not explicitly rely on annotator information performed better under weak data perspectivism compared to both strong data perspectivism and human annotations, suggesting LLM-generated views tend towards aggregation despite subjective prompting. However, for more personalized datasets tailored to strong perspectivism, the performance of LLM annotator modeling approached, but did not exceed, human annotators.

   @inproceedings{sarumi-etal-2025-impact,
     title = {The Impact of Annotator Personas on {LLM} Behavior Across the Perspectivism Spectrum},
     author = {Sarumi, Olufunke O. and Welch, Charles and Braun, Daniel and Schl{\"o}tterer, J{\"o}rg},
     editor = {Abbas, Mourad and Yousef, Tariq and Galke, Lukas},
     booktitle = {Proceedings of the 8th International Conference on Natural Language and Speech Processing (ICNLSP-2025)},
     month = aug,
     year = {2025},
     address = {Southern Denmark University, Odense, Denmark},
     publisher = {Association for Computational Linguistics},
     url = {https://aclanthology.org/2025.icnlsp-1.14/},
     pages = {121--136},
     doi = {10.48550/arXiv.2508.17164},
   }