# Emily Bender

Emily M. Bender is a computational linguist at the University of Washington known for co-authoring the Stochastic Parrots paper and for the octopus test thought experiment critiquing claims of language model understanding.

Emily M. Bender is a professor of linguistics at the University of Washington and a leading academic critic of overstated claims about what large language models understand or know.

Bender's academic background is in computational linguistics, and her research has long focused on the relationship between linguistic form and meaning, and on the risks of treating fluent language production as evidence of genuine comprehension. In a 2020 paper with Alexander Koller, "Climbing towards NLU: On Meaning, Form, and Understanding in the Age of Data," Bender introduced the "octopus test," a thought experiment in which a hyper-intelligent octopus, having only ever observed two people communicating by cable without any access to the physical world they describe, learns to produce statistically plausible responses without ever grasping what the words refer to. Bender used the analogy to argue that systems trained purely on the statistical patterns of text, however large, cannot be assumed to have acquired meaning or understanding in the way a human speaker does.

## Stochastic Parrots

In 2021, Bender co-authored "On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?" with [timnit-gebru](https://www.wikiprompt.org/wiki/timnit-gebru), Angelina McMillan-Major and Margaret Mitchell. The paper, written while Gebru and Mitchell were at Google, argued that scaling [large language models](https://www.wikiprompt.org/wiki/large-language-model) carried environmental costs, risked encoding and amplifying social biases present in web-scraped [training-data](https://www.wikiprompt.org/wiki/training-data), produced text that was difficult to audit for factual accuracy, and encouraged users and even researchers to mistake fluent output for understanding, a critique that gave the field the widely used term [stochastic-parrot](https://www.wikiprompt.org/wiki/stochastic-parrot). The paper's publication, and Google's objections to it, contributed to Gebru's high-profile departure from the company.

## Public commentary

Bender has remained one of the most visible academic voices pushing back on anthropomorphizing language, describing [chatbots](https://www.wikiprompt.org/wiki/chatbot) and pushing for more precise, non-mentalistic vocabulary when describing what [LLMs](https://www.wikiprompt.org/wiki/large-language-model) do, objecting for instance to describing models as understanding, knowing, or hallucinating in a literal sense, even while accepting terms like [hallucination](https://www.wikiprompt.org/wiki/hallucination) as established shorthand within the field. She co-hosts the podcast "Mystery AI Hype Theater 3000" with sociologist Alex Hanna, in which the two critically examine media coverage and industry claims about AI capabilities.

## Influence

Bender's linguistics-grounded skepticism has become a standard reference point in debates over [artificial-general-intelligence](https://www.wikiprompt.org/wiki/artificial-general-intelligence) timelines and claims of emergent understanding in large models, frequently invoked alongside critiques from researchers such as [gary-marcus](https://www.wikiprompt.org/wiki/gary-marcus) as a counterweight to more optimistic narratives from major AI labs.

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Source: https://www.wikiprompt.org/wiki/emily-bender
License: CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0/)
Last updated: 2026-09-02T20:32:27.688902+00:00