Emily Bender Sets the Record Straight on “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 blah.” So what do you mean by “it”? And then they’ll say, “oh, I mean Claude” or ChatGPT or Gemini, so they are talking about these chatbots. But then other people will say, “You can’t say AI is all bad, because what about AlphaFold ?” So yes, for many people, they are talking about chatbots built on top of large language models, but [they’re] also not really clear that those things are separate from something like AlphaFold. And when we have news reporting that says, “scientists use AI to discover a new drug ,” well, what did they use? If what they’re talking about is something much more narrow, maybe it’s protein folding, maybe it’s some other kind of statistical modeling [like in] weather modeling . That’s a very different kind of technology than ChatGPT. Do you think there’s a value to an umbrella term like “artificial intelligence”? Bender: Well, there’s a value to people who are trying to sell this—so to the tech companies trying to raise their valuations. Also, the way research funding is set up right now, it is very hard to get funded if you don’t call what you’re doing artificial intelligence. That I think is a net negative, but for any individual trapped in that system, that can have value in the moment. How Stochastic Parrots Have Been Misunderstood What are the most common misconceptions about the stochastic parrots metaphor? Bender: I think one of the biggest ones is, “Bender says, AI is a stochastic parrot.” That paper was written in late 2020. We were talking about large language models. I’m pretty sure the word AI comes up only once at the very end, and that’s talking about how, if you’re going to develop systems that are meant to do things like what people do, you have to be very careful that you are not creating something that can be mistaken for a person. The fact that these systems are designed to mimic the way we use language makes it very easy for people to mistake them for other people. So