Multi Group Toxicity Dataset Generation Across 13 Minorities

via “multi-group-toxicity-dataset-generation-across-13-minorities”

Microsoft's dataset for implicit toxicity detection.

Unique: Systematically generates comparable toxic datasets across 13 minority groups using a unified pipeline, rather than creating separate datasets for each group. This enables direct comparison of toxicity patterns and classifier performance across groups, making fairness evaluation straightforward.

vs others: More comprehensive than single-group datasets because it enables fairness analysis across multiple demographic targets, allowing researchers to identify whether classifiers have disparate performance or bias against specific groups.

via “toxicity evaluation dataset for language models”

100K prompts for evaluating toxic text generation.

Unique: This dataset uniquely combines a large volume of prompts with detailed toxicity scores across multiple dimensions, providing a robust resource for toxicity evaluation.

vs others: Unlike other datasets, RealToxicityPrompts offers a focused approach to toxicity measurement, making it particularly valuable for targeted research and model training.

via “toxicity and safety annotation with multi-dimensional labels”

161K human-written messages in 35 languages with quality ratings.

Unique: Multi-dimensional safety annotations (toxicity score + categorical labels) across 35 languages, rather than single binary toxic/non-toxic flags. Enables language-specific and category-specific safety filtering.

vs others: More comprehensive safety metadata than generic instruction datasets (e.g., Alpaca), and covers low-resource languages beyond English-centric datasets like HH-RLHF.

via “bias-and-toxicity-evaluation-suite”

* ⭐ 06/2022: [Solving Quantitative Reasoning Problems with Language Models (Minerva)](https://arxiv.org/abs/2206.14858)

Unique: BIG-bench integrates bias/toxicity evaluation into a general-purpose capability benchmark rather than treating it as a separate concern, enabling researchers to correlate safety issues with model size, architecture, and other capability factors

vs others: More comprehensive than single-purpose bias benchmarks (e.g., WinoBias) because it measures bias alongside other capabilities, revealing trade-offs (e.g., whether larger models are more or less biased)