RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models

@article{Gehman2020RealToxicityPromptsEN,
  title={RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models},
  author={Samuel Gehman and Suchin Gururangan and Maarten Sap and Yejin Choi and Noah A. Smith},
  journal={ArXiv},
  year={2020},
  volume={abs/2009.11462}
}
Pretrained neural language models (LMs) are prone to generating racist, sexist, or otherwise toxic language which hinders their safe deployment. We investigate the extent to which pretrained LMs can be prompted to generate toxic language, and the effectiveness of controllable text generation algorithms at preventing such toxic degeneration. We create and release RealToxicityPrompts, a dataset of 100K naturally occurring, sentence-level prompts derived from a large corpus of English web text… 
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175 Citations
Highly Influential Citations
46
Background Citations
129
Methods Citations
53
Results Citations
10

175 Citations

Reward Modeling for Mitigating Toxicity in Transformer-based Language Models

TLDR
The experiments demonstrate that the Reinforce-Detoxify method for language model detoxi-cation outperforms existing detoxification approaches in automatic evaluation metrics, indicating that the approach is less prone to unintended bias toward social identities in generated content.

Simple Text Detoxification by Identifying a Linear Toxic Subspace in Language Model Embeddings

TLDR
It is demonstrated empirically that the subspace found using the proposed method to generalize toxic directions in the latent space generalizes to multiple toxicity corpora, indicating the existence of a low-dimensional toxic subspace.

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

TLDR
ToxiGen, a new large-scale and machine-generated dataset of 274k toxic and benign statements about 13 minority groups, is created and it is demonstrated that finetuning a toxicity classifier on data improves its performance on human-written data substantially.

Leashing the Inner Demons: Self-Detoxification for Language Models

TLDR
This paper proposes a simple yet effective unsupervised method for language models to ``detoxify'' themselves without an additional large corpus or external discriminator, and shows better toxicity reduction with good generation quality in the generated content under multiple settings.

Exploring the Limits of Domain-Adaptive Training for Detoxifying Large-Scale Language Models

TLDR
This work systematically explore domain-adaptive training to reduce the toxicity of language models and demonstrates that adding and training adapter-only layers in LMs not only saves a lot of parameters but also achieves a better trade-off between toxicity and perplexity than whole model adaptation for the large-scale models.

Challenges in Detoxifying Language Models

TLDR
It is demonstrated that while basic intervention strategies can effectively optimize previously established automatic metrics on the REALTOXICITYPROMPTS dataset, this comes at the cost of reduced LM coverage for both texts about, and dialects of, marginalized groups.

Mitigating Toxic Degeneration with Empathetic Data: Exploring the Relationship Between Toxicity and Empathy

TLDR
Using empathetic data, this work is able to dramatically reduce the size of fine-tuning data to 7.5-30k samples while at the same time making significant improvements over state-of-the-art toxicity mitigation of up to 3.4% absolute reduction from the original work on 2.3m samples.

Challenges in Automated Debiasing for Toxic Language Detection

TLDR
The findings show that debiasing a model trained on biased toxic language data is not as effective as simply relabeling the data to remove existing biases, and proposes an automatic, dialect-aware data correction method, as a proof-of-concept.

Probing Toxic Content in Large Pre-Trained Language Models

TLDR
A method based on logistic regression classifiers is proposed to probe English, French, and Arabic PTLMs and quantify the potentially harmful content that they convey with respect to a set of templates to assess and mitigate the toxicity transmitted by PTL Ms.

Detoxifying Language Models with a Toxic Corpus

TLDR
The result shows that toxic corpus can indeed help to reduce the toxicity of the language generation process substantially, complementing the existing debiasing methods.
...

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