Abstract from a reviewer (source reddit /r/ml) Abstract The past three years of work in natural language processing have been characterized by the development

Abstract from a reviewer (source reddit /r/ml) Abstract The past three years of work in natural language processing have been characterized by the development and deployment of ever larger language models, especially for English. GPT-2, GPT-3, BERT and its variants have pushed the boundaries of the possible both through architectural innovations and through sheer size. Using these pre- trained models and the methodology of fine-tuning them for specific tasks, researchers have extended the state of the art on a wide array of tasks as measured by leaderboards on specific benchmarks for English. In this paper, we take a step back and ask: How big is too big? What are the possible risks associated with this technology and what paths are available for mitigating those risks? We end with recommendations including weighing the environmental and financial costs first, investing resources into curating and carefully documenting datasets rather than ingesting everything on the web, carrying out pre-development exercises evaluating how the planned approach fits into research and development goals and supports stakeholder values, and encouraging research directions beyond ever larger language models. Anyone have the actual paper?