Large language models (LLMs) like OpenAI’s ChatGPT all suffer from the same problem: they make stuff up.
The mistakes range from strange and innocuous — like claiming that the Golden Gate Bridge was transported across Egypt in 2016 — to highly problematic, even dangerous.
A mayor in Australia recently threatened to sue OpenAI because ChatGPT mistakenly claimed he pleaded guilty in a major bribery scandal. Researchers have found that LLM hallucinations can be exploited to distribute malicious code packages to unsuspecting software developers. And LLMs frequently give bad mental health and medical advice, like that wine consumption can “prevent cancer.”
This tendency to invent “facts” is a phenomenon known as hallucination, and it happens because of the way today’s LLMs — and all generative AI models, for that matter — are developed and trained.
Training models
Generative AI models have no real intelligence — they’re statistical systems that predict words, images, speech, music or other data. Fed an enormous number of examples, usually sourced from the public web, AI models learn how likely data is to occur based on patterns, including the context of any surrounding data.
For example, given a typical email ending in the fragment “Looking forward…”, an LLM might complete it with “… to hearing back” — following the pattern of the countless emails it’s been trained on. It doesn’t mean the LLM is looking forward to anything.
“The current framework of training LLMs involves concealing, or ‘masking,’ previous words for context” and having the model predict which words should replace the concealed ones, Sebastian Berns, a Ph.D. researchers at Queen Mary University of London, told TechCrunch in an email interview. “This is conceptually similar to using predictive text in iOS and continually pressing one of the suggested next words.”