On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?

by Emily M.Bender, Timnit Gebru, Angelina McMillan-Major

The past 3 years of work in NLP have been characterized by the development and deployment of ever larger language models, especially for English. BERT, its variants, GPT-2/3, and others, most recently Switch-C, have pushed the boundaries of the possible both through architectural innovations and through sheer size. Using these pretrained models and the methodology off 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 provide 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.

Genres: Artificial Intelligence

14 pages, ebook

Published March 1, 2021

Reviews

[Manny]

- It says that large language models don't really understand anything, are full of bias, become even worse when exposed to unfiltered internet content, have an excessive carbon footprint, and are generally a Bad Thing.

- Compared to what?

- Uh, I guess humans?