Papers by John J. Guerrerio
When Debiasing Backfires: Counterintuitive Side Effects of Preprocessing-Based Stereotype Mitigation (2026.findings-acl)
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| Challenge: | Preprocessing-based methods for stereotype mitigation are widely used in NLP . preprocessing methods cause unintended shifts in attention flow, authors say . |
| Approach: | They propose to use preprocessing-based methods to reduce stereotypes for targeted groups . they find that stereotyping or counter-stereotyping can increase for other demographics . |
| Outcome: | The proposed methods often induce unintended shifts across demographics, the authors show . they show that such side effects are not accompanied by large changes in attention flow . |
Scalable and Culturally Specific Stereotype Dataset Construction via Human-LLM Collaboration (2025.emnlp-main)
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| Challenge: | Existing approaches for detecting and mitigating embedded stereotypes rely on carefully annotated datasets like StereoSet and CrowS-Pairs, which are only in English and reflect stereotypes from a few English-speaking countries. Existing datasets, especially translation-based ones, often overlook such cultural distinctions. |
| Approach: | They propose a cost-efficient human-LLM collaborative annotation framework to construct a Spanish-language stereotype dataset spanning multiple Spanish-speaking countries. |
| Outcome: | The proposed framework can identify nuanced, region-specific biases across Spanish-supporting LLMs and is adaptable to other languages and regions. |