What Emily Bender Really Meant by “Stochastic Parrots”

In March 2021, a group of four linguists and computer scientists published their now legendary paper “On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜”

The paper received significant attention at the time (in part because Google fired two of the authors, Timnit Gebru and Margaret Mitchell, shortly before its publication). It argued that large language models generate text by statistically predicting likely sequences of words rather than understanding what they are saying—a process the authors captured with the metaphor of a “stochastic parrot,” a system that repeats patterns without comprehension. And over the past five years, the analogy has spread well beyond the academic field where it originated, spawning debates and inspiring projects such as a shoulder-mounted robot named the Stochastic Parrot.

But that wider usage has also led to misconceptions about what the phrase originally meant. Lead author Emily M. Bender, a professor of computational linguistics at the University of Washington, recently wrote a blog post to debunk common misconceptions about the paper on its five-year anniversary.

Bender spoke with IEEE Spectrum about these misconceptions, the field of computational linguistics, and the current discourse around artificial intelligence.

What’s Wrong With the Term “Artificial Intelligence”

How would you describe your work as a computational linguist?

Emily M. Bender: Linguistics, very generally, is the study of how language works and how we work with language. I contribute to that, and I also work in computational linguistics, training students who are going to go on to build language technology.

Language technology actually stands alone as valuable and interesting, independent of whether or not someone wants to use it for their project of artificial intelligence. Language technology includes things like automatic transcription, machine translation, spell check. And a lot of the work that I do personally, when I am building things, has to do with building machine-readable, but also human-readable grammars that model linguistic phenomena in different languages. That’s about using computers in the service of linguistic hypothesis testing.

You’ve argued that the term “artificial intelligence” obscures more than it clarifies. Why?

Bender: Many reasons. I think that it makes it difficult to actually have good discussions about technology and make wise decisions about it, if the way we’re talking about it doesn’t make clear what the technology is. The phrase “artificial intelligence” both groups together disparate technologies and oversells what each one of them can do. So if we are trying to decide whether or not to use something, how to regulate something, we are much better off with clearer descriptions.

In general conversation, AI has become almost synonymous with “chatbots” or “LLMs.” Is that a problem?

Bender: For many people, they’ll say, “I use it to do blah blah…