Rethinking Toxicity Evaluation in Large Language Models: A Multi-Label Perspective
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866914097903173632 |
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| author | Kou, Zhiqiang Chen, Junyang Cai, Xin-Qiang Xie, Ming-Kun Liu, Biao Wang, Changwei Feng, Lei Jia, Yuheng Niu, Gang Sugiyama, Masashi Geng, Xin |
| author_facet | Kou, Zhiqiang Chen, Junyang Cai, Xin-Qiang Xie, Ming-Kun Liu, Biao Wang, Changwei Feng, Lei Jia, Yuheng Niu, Gang Sugiyama, Masashi Geng, Xin |
| contents | Large language models (LLMs) have achieved impressive results across a range of natural language processing tasks, but their potential to generate harmful content has raised serious safety concerns. Current toxicity detectors primarily rely on single-label benchmarks, which cannot adequately capture the inherently ambiguous and multi-dimensional nature of real-world toxic prompts. This limitation results in biased evaluations, including missed toxic detections and false positives, undermining the reliability of existing detectors. Additionally, gathering comprehensive multi-label annotations across fine-grained toxicity categories is prohibitively costly, further hindering effective evaluation and development. To tackle these issues, we introduce three novel multi-label benchmarks for toxicity detection: \textbf{Q-A-MLL}, \textbf{R-A-MLL}, and \textbf{H-X-MLL}, derived from public toxicity datasets and annotated according to a detailed 15-category taxonomy. We further provide a theoretical proof that, on our released datasets, training with pseudo-labels yields better performance than directly learning from single-label supervision. In addition, we develop a pseudo-label-based toxicity detection method. Extensive experimental results show that our approach significantly surpasses advanced baselines, including GPT-4o and DeepSeek, thus enabling more accurate and reliable evaluation of multi-label toxicity in LLM-generated content. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_15007 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Rethinking Toxicity Evaluation in Large Language Models: A Multi-Label Perspective Kou, Zhiqiang Chen, Junyang Cai, Xin-Qiang Xie, Ming-Kun Liu, Biao Wang, Changwei Feng, Lei Jia, Yuheng Niu, Gang Sugiyama, Masashi Geng, Xin Computation and Language Artificial Intelligence Large language models (LLMs) have achieved impressive results across a range of natural language processing tasks, but their potential to generate harmful content has raised serious safety concerns. Current toxicity detectors primarily rely on single-label benchmarks, which cannot adequately capture the inherently ambiguous and multi-dimensional nature of real-world toxic prompts. This limitation results in biased evaluations, including missed toxic detections and false positives, undermining the reliability of existing detectors. Additionally, gathering comprehensive multi-label annotations across fine-grained toxicity categories is prohibitively costly, further hindering effective evaluation and development. To tackle these issues, we introduce three novel multi-label benchmarks for toxicity detection: \textbf{Q-A-MLL}, \textbf{R-A-MLL}, and \textbf{H-X-MLL}, derived from public toxicity datasets and annotated according to a detailed 15-category taxonomy. We further provide a theoretical proof that, on our released datasets, training with pseudo-labels yields better performance than directly learning from single-label supervision. In addition, we develop a pseudo-label-based toxicity detection method. Extensive experimental results show that our approach significantly surpasses advanced baselines, including GPT-4o and DeepSeek, thus enabling more accurate and reliable evaluation of multi-label toxicity in LLM-generated content. |
| title | Rethinking Toxicity Evaluation in Large Language Models: A Multi-Label Perspective |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2510.15007 |