Go to ACL ARR 2025 May homepageStereoDetect: Detecting Stereotypes and Anti-stereotypes the Correct Way Using Social Psychological Underpinnings
Abstract: Stereotypes are known to have very harmful effects, making their detection critically important.
However, current research predominantly focuses on detecting and evaluating stereotypical biases, leaving the study of stereotypes in its early stages.
Our study revealed that many works have failed to clearly distinguish between stereotypes and stereotypical biases, which has significantly slowed progress in advancing research in this area.
Stereotype and Anti-stereotype detection is a problem that requires social knowledge; hence, it is one of the most difficult areas in Responsible AI.
This work investigates this task, where we propose a five-tuple definition and provide precise terminologies disentangling stereotypes, anti‑stereotypes, stereotypical bias, and general bias. We provide a conceptual framework grounded in social psychology for reliable detection. We identify key shortcomings in existing benchmarks for this task of stereotype and anti-stereotype detection. To address these gaps, we developed *StereoDetect*, a well curated, definition‑aligned benchmark dataset designed for this task. We show that language models with fewer than 10 billion parameters frequently misclassify anti‑stereotypes and fail to recognize neutral overgeneralizations. We demonstrate StereoDetect’s effectiveness through multiple qualitative and quantitative comparisons with existing benchmarks and models fine-tuned on them.
Paper Type: Long
Research Area: Computational Social Science and Cultural Analytics
Research Area Keywords: language/cultural bias analysis, sociolinguistics, NLP tools for social analysis, hate-speech detection, bias/toxicity, misinformation detection and analysis
Contribution Types: Model analysis & interpretability, Publicly available software and/or pre-trained models, Data resources, Data analysis, Theory
Languages Studied: English
Submission Number: 6990
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