Learning Sexism Detection Using Multi-Agent Perspectivist Preference Optimization
arXiv:2608.04056v1 Announce Type: cross Abstract: When people label text for sexism, they often disagree, and not because some of them are wrong: they genuinely perceive sexism differently. Most NLP systems discard this disagreement by collapsing it into a majority vote. We propose the Multi-Agent Perspectivist Preference Optimization (MAP-PO) framework to keep these different perspectives. On the EXIST 2024 dataset of labeled English and Spanish tweets, we first cluster annotators by their labe
Record details
Published: 6 August 2026
Source: arXiv cs.CY
Category: Research
Topics: Agents & autonomy
Retrieved: 6 August 2026
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ethics.ai (6 August 2026), “Learning Sexism Detection Using Multi-Agent Perspectivist Preference Optimization,” evidence record 16642, https://ethics.ai/record/16642 (originally published by arXiv cs.CY).
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