Embracing Diversity: A Multi-Perspective Approach with Soft Labels

• URL: http://arxiv.org/abs/2503.00489v1
• Date: Sat, 01 Mar 2025 13:33:38 GMT
• Title: Embracing Diversity: A Multi-Perspective Approach with Soft Labels
• Authors: Benedetta Muscato, Praveen Bushipaka, Gizem Gezici, Lucia Passaro, Fosca Giannotti, Tommaso Cucinotta,
• Abstract summary: We propose a new framework for designing perspective-aware models on stance detection task, in which multiple annotators assign stances based on a controversial topic.<n>Results show that the multi-perspective approach yields better classification performance (higher F1-scores)
• Score: 3.529000007777341
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Prior studies show that adopting the annotation diversity shaped by different backgrounds and life experiences and incorporating them into the model learning, i.e. multi-perspective approach, contribute to the development of more responsible models. Thus, in this paper we propose a new framework for designing and further evaluating perspective-aware models on stance detection task,in which multiple annotators assign stances based on a controversial topic. We also share a new dataset established through obtaining both human and LLM annotations. Results show that the multi-perspective approach yields better classification performance (higher F1-scores), outperforming the traditional approaches that use a single ground-truth, while displaying lower model confidence scores, probably due to the high level of subjectivity of the stance detection task.

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