A Holistic Approach to Undesired Content Detection in the Real World
Todor Markov, Chong Zhang, Sandhini Agarwal, Florentine Eloundou Nekoul, Theodore Lee, Steven Adler, Angela Jiang, Lilian Weng
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.
BibTeX
@article{Markov_Zhang_Agarwal_Eloundou Nekoul_Lee_Adler_Jiang_Weng_2023, title={A Holistic Approach to Undesired Content Detection in the Real World}, volume={37}, url={https://ojs.aaai.org/index.php/AAAI/article/view/26752}, DOI={10.1609/aaai.v37i12.26752}, abstractNote={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.}, number={12}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Markov, Todor and Zhang, Chong and Agarwal, Sandhini and Eloundou Nekoul, Florentine and Lee, Theodore and Adler, Steven and Jiang, Angela and Weng, Lilian}, year={2023}, month={Jun.}, pages={15009-15018} }