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

Abstract

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 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 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.

The authors' own abstract, copied from semanticscholar.org.

How to cite it

Built from the details listed on this page. Check them against the paper before you submit.

Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?. Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (FAccT 2021). https://doi.org/10.1145/3442188.3445922

Bender, Emily M., et al. "On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?." Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (FAccT 2021), 2021, https://doi.org/10.1145/3442188.3445922.

@article{bender2021on,
  title = {On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?},
  author = {Emily M. Bender and Timnit Gebru and Angelina McMillan-Major and Shmargaret Shmitchell},
  journal = {Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (FAccT 2021)},
  year = {2021},
  url = {https://doi.org/10.1145/3442188.3445922},
}

What it is

Argues that ever-larger language models carry costs that scale with them: environmental cost, unauditable training data, encoded bias, and the illusion of understanding. The source of the phrase “stochastic parrot.” Free to read on the ACM site.

Topics