A systematic pipeline for diagnosing and reducing gender-stereotype bias in Japanese PLMs for sentiment analysis

Pre-trained language models (PLMs) are widely used in sentiment analysis, but they may inherit gender-stereotypical bias from large-scale text corpora and transfer such bias to downstream sentiment predictions. Despite growing attention to gender-stereotypical bias in PLMs, existing studies predominantly focus on English corpora and static word embeddings, limiting understanding of how such bias affects sentiment analysis models and the effectiveness of mitigation strategies. In this study, we p