A Holistic Approach to Undesired Content Detection in the Real World

Autor:	Markov, Todor, Zhang, Chong, Agarwal, Sandhini, Eloundou, Tyna, Lee, Teddy, Adler, Steven, Jiang, Angela, Weng, Lilian
Rok vydání:	2022
Předmět:	Computer Science - Computation and Language Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: Oral presentation at AAAI-23
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2208.03274 Zobrazit plný text záznamu View this record from Arxiv