Research paper FAccT '21: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency Subscription may be required

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

Bibliographic record for On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜.
Bibliographic record · FAccT '21: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency. View in context ↗

In this reference

Emily M. Bender, Timnit Gebru, Angelina McMillan-Major, and others: On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜.

Cite the original work

Emily M. Bender & Timnit Gebru & Angelina McMillan-Major & Shmargaret Shmitchell (2021). On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜 [Research paper]. FAccT '21: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency. https://dl.acm.org/doi/10.1145/3442188.3445922

View BibTeX
@misc{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},
  title = {{On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜}},
  howpublished = {FAccT '21: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency},
  url = {https://dl.acm.org/doi/10.1145/3442188.3445922},
  year = {2021},
  doi = {10.1145/3442188.3445922}
}
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