Toxicity in online content, including content generated by language models, has become a critical concern due to its potential for negative psychological and social impact. This paper introduces TRuST, a comprehensive dataset designed to improve toxicity detection that merges existing datasets, and has labels for toxicity, target social group, and toxic spans. It includes a diverse range of target groups such as ethnicity, gender, religion, disability, and politics, with both human/machine-annotated and human machine-generated data. We benchmark state-of-the-art large language models (LLMs) on toxicity detection, target group identification, and toxic span extraction. We find that fine-tuned models consistently outperform zero-shot and few-shot prompting, though performance remains low for certain social groups. Further, reasoning capabilities do not significantly improve performance, indicating that LLMs have weak social reasoning skills.
View on arXiv@article{atil2025_2506.02326,
title={ Something Just Like TRuST : Toxicity Recognition of Span and Target },
author={ Berk Atil and Namrata Sureddy and Rebecca J. Passonneau },
journal={arXiv preprint arXiv:2506.02326},
year={ 2025 }
}