Since the launch of ChatGPT in late 2022 we’ve known that artificial intelligence language models are prone to spewing falsehoods, otherwise known as hallucinations.
The AI companies have been telling us that it’s a problem that can be cured. And with the use of technologies like retrieval-augmented generation (in which the AI calls on a database of reliable information), hallucinations have indeed decreased in many contexts. But they persist, as a recent Wired investigation of the Perplexity AI search tool illustrated. The Wired story features an especially bold headline: “Perplexity Is a Bullshit Machine.”
The use of “bullshit” made more than a grabby headline; it was a reference to recently published research from a trio of philosophy professors at Glasgow University. The report, titled “ChatGPT is bullshit,” argues that calling the false output of large language models hallucination is misleading; what LLMs are really spouting, they argue, is more like bullshit. And not just any bullshit: Bullshit as defined by the late moral philosopher Harry Frankfurt in his 2005 bestseller, On Bullshit.
Frankfurt’s book is principally concerned with defining the difference between a “liar” and a “bullshitter.” A bullshitter (aka “bullshit artist”) doesn’t use facts to come off as credible and therefore persuasive, he explained, but is content to say things that sound true to get the same result. For example, a used-car salesman who is practicing bullshit uses a set of talking points he thinks will lead someone to buy a car. Some of the talking points may be true, some may be false; to him it doesn’t matter: He would use the same set of points whether they happened to be true or not. “[He] does not reject the authority of the truth, as the liar does, and oppose himself to it,” Frankfurt wrote. “He pays no attention to it at all. By virtue of this, bullshit is a greater enemy of the truth than lies are.”
In their recent report, the Glasgow researchers—Michael Townsen Hicks, James Humphries, and Joe Slater—argue that Frankfurt’s bullshit definition fits the behavior of LLMs better than the term hallucinate. In order to hallucinate, the researchers argue, one must have some awareness or regard for the truth; LLMs, by contrast, work with probabilities, not binary correct/incorrect judgments. Based on a huge many-dimensional map of words created by processing huge amounts of text, LLMs decide which words (based on meaning and current context) would most likely follow from the words used in a prompt. They’re inherently more concerned with sounding truthy than delivering a factually correct response, the researchers conclude.
“ChatGPT and other LLMs produce bullshit, in the sense that they don’t care about the truth of their outputs,” Hicks said in an email to Fast Company. “Thinking of LLMs this way provides a more accurate way of thinking about what they are doing, and thereby allows consumers and regulators to better understand why they often get things wrong.”
Importantly, the LLM doesn’t always choose the word that is statistically most likely to follow, the researchers point out. Letting the model choose between a set of more or less likely candidates for the next word gives the output an unexpected, creative, or even human quality. This quality can even be modulated using a control that AI developers call “temperature.” But dialing up the model’s temperature increases the chances it will generate falsehoods.
