In machine learning, the term stochastic parrot is a metaphor to describe the theory that large language models, though able to generate plausible language, do not understand the meaning of the language they process.[1][2] The term was coined by Emily M. Bender[2][3] in the 2021 artificial intelligence research paper "On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜" by Bender, Timnit Gebru, Angelina McMillan-Major, and Margaret Mitchell.[4]
^Lindholm et al. 2022, pp. 322–3.
^ abUddin, Muhammad Saad (April 20, 2023). "Stochastic Parrots: A Novel Look at Large Language Models and Their Limitations". Towards AI. Retrieved 2023-05-12.
^Weil, Elizabeth (March 1, 2023). "You Are Not a Parrot". New York. Retrieved 2023-05-12.
^Bender, Emily M.; Gebru, Timnit; McMillan-Major, Angelina; Shmitchell, Shmargaret (2021-03-01). "On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜". Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency. FAccT '21. New York, NY, USA: Association for Computing Machinery. pp. 610–623. doi:10.1145/3442188.3445922. ISBN 978-1-4503-8309-7. S2CID 232040593.
In machine learning, the term stochasticparrot is a metaphor to describe the theory that large language models, though able to generate plausible language...
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Angelina; Shmitchell, Shmargaret (1 March 2021). "On the Dangers of StochasticParrots: Can Language Models be Too Big? 🦜". Proceedings of the 2021 ACM...
departure was her refusal to retract a paper entitled "On the Dangers of StochasticParrots: Can Language Models Be Too Big?" and a related ultimatum she made...
her supporters. Gebru was a co-author of a paper On the Dangers of StochasticParrots: Can Language Models Be Too Big? the publication of which resulted...
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