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arXiv · 2208.03274

A Holistic Approach to Undesired Content Detection in the Real World

Todor Markov·Chong Zhang·Sandhini Agarwal·Tyna Eloundou·Teddy Lee·Steven Adler·Angela Jiang·Lilian Weng
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Abstract

We present a holistic approach to building a robust and useful natural language classification system for real-world content moderation. The success of such a system relies on a chain of carefully designed and executed steps, including the design of content taxonomies and labeling instructions, data quality control, an active learning pipeline to capture rare events, and a variety of methods to make the model robust and to avoid overfitting. Our moderation system is trained to detect a broad set of categories of undesired content, including sexual content, hateful content, violence, self-harm, and harassment. This approach generalizes to a wide range of different content taxonomies and can be used to create high-quality content classifiers that outperform off-the-shelf models.

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Todor Markov, Chong Zhang, Sandhini Agarwal, Tyna Eloundou, Teddy Lee, Steven Adler, Angela Jiang, Lilian Weng. 2022-08-05. A Holistic Approach to Undesired Content Detection in the Real World. https://arxiv.org/abs/2208.03274

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