@inproceedings{11568_1322167,
 abstract = {In subjective tasks like stance detection, diverse human perspectives are often simplified into a single ground truth through label aggregation i.e. majority voting, potentially marginalizing minority viewpoints. This paper presents a Multi-Perspective framework for stance detection that explicitly incorporates annotation diversity by using soft labels derived from both human and large language model (LLM) annotations. Building on a stance detection dataset focused on controversial topics, we augment it with document summaries and new LLM-generated labels. We then compare two approaches: a baseline using aggregated hard labels, and a multi-perspective model trained on disaggregated soft labels that capture annotation distributions. Our findings show that multi-perspective models consistently outperform traditional baselines (higher F1-scores), with lower model confidence, reflecting task subjectivity. This work highlights the importance of modeling disagreement and promotes a shift toward more inclusive, perspective-aware NLP systems.},
 address = {Amsterdam},
 author = {Muscato, Benedetta and Bushipaka, Praveen and Gezici, Gizem and Passaro, Lucia and Giannotti, Fosca and Cucinotta, Tommaso},
 booktitle = {Proceedings of {{HHAI}} 2025: {{The}} 4th International Conference Series on Hybrid Human-Artificial Intelligence},
 doi = {10.3233/FAIA250654},
 isbn = {978-1-64368-611-0},
 keywords = {Annotation Diversity,Human-Centered AI,Natural Language Processing,Perspectivism,Responsible AI,Stance Detection},
 pages = {370--384},
 publisher = {IOS Press},
 title = {Embracing Diversity: A Multi-Perspective Approach with Soft Labels},
 year = {2025}
}