The paper’s official title is “Climbing Towards NLU: On Meaning, Form, and Understanding in the Age of Data.” NLU stands for “natural-language understanding.” How should we interpret the natural-sounding (i.e., humanlike) words that come out of LLMs? The models are built on statistics. They work by looking for patterns in huge troves of text and then using those patterns to guess what the next word in a string of words should be. They’re great at mimicry and bad at facts. Why? LLMs, like the octopus, have no access to real-world, embodied referents. This makes LLMs beguiling, amoral, and the Platonic ideal of the bullshitter, as philosopher Harry Frankfurt, author of On Bullshit, defined the term. Bullshitters, Frankfurt argued, are worse than liars. They don’t care whether something is true or false. They care only about rhetorical power — if a listener or reader is persuaded.
Bender is 49, unpretentious, stylistically practical, and extravagantly nerdy — a woman with two cats named after mathematicians who gets into debates with her husband of 22 years about whether the proper phrasing is “she doesn’t give a fuck” or “she has no fucks left to give.” In the past few years, in addition to running UW’s computational-linguistics master’s program, she has stood on the threshold of our chatbot future, screaming into the deafening techno beat of AI hype. To her ear, the overreach is nonstop: No, you shouldn’t use an LLM to “unredact” the Mueller Report; no, an LLM cannot meaningfully testify in the U.S. Senate; no, chatbots cannot “develop a near-precise understanding of the person on the other end.”
Please do not conflate word form and meaning. Mind your own credulity. These are Bender’s rallying cries. The octopus paper is a fable for our time. The big question underlying it is not about tech. It’s about us. How are we going to handle ourselves around these machines?