Recently, there has been considerable interest in large language models: machine learning systems which produce human-like text and dialogue. Applications of these systems have been plagued by persistent inaccuracies in their output; these are often called “AI hallucinations”. We argue that these fa...
Recently, there has been considerable interest in large language models: machine learning systems which produce human-like text and dialogue. Applications of these systems have been plagued by persistent inaccuracies in their output; these are often called “AI hallucinations”. We argue that these falsehoods, and the overall activity of large language models, is better understood as bullshit in the sense explored by Frankfurt (On Bullshit, Princeton, 2005): the models are in an important way indifferent to the truth of their outputs. We distinguish two ways in which the models can be said to be bullshitters, and argue that they clearly meet at least one of these definitions. We further argue that describing AI misrepresentations as bullshit is both a more useful and more accurate way of predicting and discussing the behaviour of these systems.
It's bullshitting, because that's what you designed it to do. You designed it to generate seemingly authoritative text "with a blatant disregard for truth and logical coherence," i.e., to bullshit.
I confess myself a bit baffled by people who act like "how to interact with ChatGPT" is a useful classroom skill. It's not a word processor or a spreadsheet; it doesn't have documented, well-defined, reproducible behaviors. No, it's not remotely analogous to a calculator. Calculators are built to be right, not to sound convincing. It's a bullshit fountain. Stop acting like you're a waterbender making emotive shapes by expressing your will in the medium of liquid bullshit. The lesson one needs about a bullshit fountain is not to swim in it.
Imagine still not realizing what a useful skill bullshitting is. Literally, hundreds of millions of people are professional bullshitters. So many people go do bullshit every day, all day. Having a machine that can produce the same or better bullshit than them frees them from suffering through doing all that bullshit. I can't think of something that is more bullshit than pretending like there is no benefit from automating the bullshit out of our lives.
Except it's not really being automated out of our lives, is it? I find it hard to imagine how increasing the rate at which bullshit can be produced leads to a world with less bullshit in it.
Problem is that it doesn't automate away the bullshit in our lives. We're creating even more bullshit that we're forced to deal with online and at our jobs. Sure we can use the bullshit generator to respond to bullshit, but how do you know what's bullshit in the first place, are you going to ask your bullshit generator to sort that out for you as well?
Control the language and you control the thought. I pitched a fit when "hallucinate" was put forward by the tech giants to describe their LLMs' falsehoods, and it mostly fell on deaf ears in my circles. Hallucinating isn't what these things do. They bullshit.
Hallucination also hid that literally everything they produce is a 'hallucination' because that's how they work. "Bullshit" is much more apt, as a bullshitter is sometimes and even often right.
The use of anthropomorphic language to describe LLMs is infuriating. I don't even think bullshit is a good term, because among other things it implies intent or agency. Maybe the LLM produces something that you could call bullshit, but to bulshit is a human thing and I'd argue that only reason that what the LLM is producing can be called bullshit is because there's a person involved in the process.
Probably better to think about it in terms of lossy compression. Even if that's not quite right, it's less inaccurate and it doesn't obfuscate difference between what the person brings to the table and what the LLM is actually doing.
We further argue that describing AI misrepresentations as bullshit is both a more useful and more accurate way of predicting and discussing the behaviour of these systems.