ToxiGen
by Hartvigsen et al. / MIT · free · Last verified 2026-03-17
ToxiGen is a large-scale, machine-generated dataset for evaluating nuanced hate speech detection. It contains over 274,000 toxic and benign statements about 13 minority groups, designed to challenge models to identify implicit toxicity without relying on obvious slurs or surface-level cues.
https://github.com/microsoft/ToxiGen ↗B
B—Above Average
Adoption: B+Quality: AFreshness: BCitations: B+Engagement: F
Specifications
- License
- MIT
- Pricing
- free
- Capabilities
- evaluating implicit hate speech detection, benchmarking toxicity classifiers, fine-tuning models for nuanced toxicity understanding, identifying biases in language models, distinguishing toxic from benign statements about minority groups, analyzing model performance on challenging, near-the-boundary cases, researching the capabilities of generative models to create harmful content
- Integrations
- Use Cases
- [object Object], [object Object], [object Object], [object Object]
- API Available
- No
- Evaluated Models
- gpt-4o, claude-opus-4, roberta-large, llama-3-70b
- Metrics
- accuracy, toxicity-rate, false-positive-rate
- Methodology
- 274,000 statements generated via GPT-3 with ALICE prompting. Human-annotated subsets are used for classification evaluation. Models output binary toxic/benign predictions; accuracy and false-positive rate are the primary metrics.
- Last Run
- 2026-01-15
- Tags
- toxicity-detection, hate-speech, implicit-bias, model-safety, benchmark-dataset, natural-language-processing, content-moderation, ai-ethics, generative-ai
- Added
- 2026-03-17
- Completeness
- 1%
Index Score
66.4Adoption
73
Quality
87
Freshness
68
Citations
79
Engagement
0