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Hallucinations have plagued OpenAI ever since it launched its blockbuster ChatGPT chatbot back in 2022.
The propensity of large language models to sound both plausible and confident about outputs that are totally wrong continues to represent a major thorn in the sides of execs who claim the AI boom is both bigger and faster than the industrial revolution.
The issue still haunts even the most sophisticated AI models today, a persistent issue unlikely to be resolved any time soon — if ever, experts warn.
It’s a particularly troublesome reality in a healthcare setting, from Google’s AI Overviews feature giving out dangerous “health” advice to hospitals deploying transcription tools that invent nonexistent medications and more.
And when it comes to analyzing radiology scans — an application for AI long championed by its advocates in the healthcare industry — the situation becomes even more concerning.
As detailed in a new, yet-to-be-peer-reviewed paper, a team of researchers at Stanford University found that frontier AI models readily generated “detailed image descriptions and elaborate reasoning traces, including pathology-biased clinical findings, for images never provided.”
In other words, the AI models happily came up with answers to questions about a supposedly accompanying image — even if the researchers never even showed it an image.
As opposed to hallucinations, which involve AI models arbitrarily filling in the gaps within a logical framework, the team coined a new term for the phenomenon: “mirage reasoning.”
The effect “involves constructing a false epistemic frame, i.e., describing a multi-modal input never provided by the user and basing the rest of the conversation on that, therefore changing the context of the task at hand,” the researchers wrote in their paper.
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