ToxiGen: A Large-Scale Machine-Generated Dataset for Adversarial and Implicit Hate Speech Detection

TL;DR

ToxiGen is a large, machine-generated dataset designed to improve hate speech detection, especially for implicit and minority group-related toxicity, by providing diverse, subtly toxic examples that enhance classifier robustness.

Contribution

The paper introduces ToxiGen, a novel large-scale dataset created using a prompting framework and adversarial decoding, covering implicit toxicity and multiple minority groups, surpassing previous human-written resources.

Findings

Finetuning classifiers on ToxiGen improves detection of human-written hate speech.

Humans struggle to distinguish machine-generated from human-written toxic text.

ToxiGen enhances classifier performance on both real and machine-generated toxicity.

Abstract

Toxic language detection systems often falsely flag text that contains minority group mentions as toxic, as those groups are often the targets of online hate. Such over-reliance on spurious correlations also causes systems to struggle with detecting implicitly toxic language. To help mitigate these issues, we create ToxiGen, a new large-scale and machine-generated dataset of 274k toxic and benign statements about 13 minority groups. We develop a demonstration-based prompting framework and an adversarial classifier-in-the-loop decoding method to generate subtly toxic and benign text with a massive pretrained language model. Controlling machine generation in this way allows ToxiGen to cover implicitly toxic text at a larger scale, and about more demographic groups, than previous resources of human-written text. We conduct a human evaluation on a challenging subset of ToxiGen and find that annotators struggle to distinguish machine-generated text from human-written language. We also find that 94.5% of toxic examples are labeled as hate speech by human annotators. Using three publicly-available datasets, we show that finetuning a toxicity classifier on our data improves its performance on human-written data substantially. We also demonstrate that ToxiGen can be used to fight machine-generated toxicity as finetuning improves the classifier significantly on our evaluation subset. Our code and data can be found at https://github.com/microsoft/ToxiGen.