Emily Bender Sets the Record Straight on “Stochastic Parrots”
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In March 2021, a notable paper titled “On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?” was published by a team of four linguists and computer scientists, including Timnit Gebru and Margaret Mitchell, shortly after their controversial dismissal from Google. The paper critiques large language models, suggesting they generate text through statistical predictions rather than genuine understanding, coining the term "stochastic parrot" to illustrate this concept. As the analogy gained traction beyond academia, it sparked debates and inspired projects, including a shoulder-mounted robot named the Stochastic Parrot.
On the five-year anniversary of the paper, lead author Emily M. Bender, a professor at the University of Washington, addressed common misconceptions surrounding the term in a recent blog post and an interview with IEEE Spectrum. Bender emphasized that the phrase specifically refers to large language models and not to all forms of artificial intelligence, which she believes oversimplifies the technology and complicates discussions about its implications.
She highlighted the importance of clear terminology in understanding and regulating technology, noting that many discussions conflate different AI applications, such as chatbots and protein folding algorithms. Bender also acknowledged that the paper overlooked significant issues, such as exploitative labor practices in data collection, which she now believes should have been included. The ongoing discourse around language models continues to evolve, reflecting the complexities of artificial intelligence and its societal impact.
Original report
Emily Bender Sets the Record Straight on “Stochastic Parrots”
IEEESpectrumAI · Gwendolyn RakThis briefing is an independently written summary based on publicly available reporting and is provided for industry information and news discovery. The original report and source publication are credited and linked where applicable. RobotToday does not claim ownership of third-party source material.
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