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Found 7 papers, 3 papers with code
Lost in Distillation: A Case Study in Toxicity Modeling
In an era of increasingly large pre-trained language models, knowledge distillation is a powerful tool for transferring information from a large model to a smaller one.
Is Your Toxicity My Toxicity? Exploring the Impact of Rater Identity on Toxicity Annotation
Next, we trained models on the annotations from each of the different rater pools, and compared the scores of these models on comments from several test sets.
A New Generation of Perspective API: Efficient Multilingual Character-level Transformers
As such, it is crucial to develop models that are effective across a diverse range of languages, usages, and styles.
Measuring and Improving Model-Moderator Collaboration using Uncertainty Estimation
Content moderation is often performed by a collaboration between humans and machine learning models.
Nuanced Metrics for Measuring Unintended Bias with Real Data for Text Classification
Unintended bias in Machine Learning can manifest as systemic differences in performance for different demographic groups, potentially compounding existing challenges to fairness in society at large.
Limitations of Pinned AUC for Measuring Unintended Bias
This report examines the Pinned AUC metric introduced and highlights some of its limitations.
Model Cards for Model Reporting
Model cards also disclose the context in which models are intended to be used, details of the performance evaluation procedures, and other relevant information.
