When German journalist Martin Bernklautyped his name and location into Microsoft’s Copilot to see how his articles would be picked up by the chatbot, the answers horrified him. Copilot’s results asserted that Bernklau was an escapee from a psychiatric institution, a convicted child abuser, and a conman preying on widowers. For years, Bernklau had served as a courts reporter and the AI chatbot had falsely blamed him for the crimes whose trials he had covered.
The accusations against Bernklau weren’t true, of course, and are examples of generative AI’s “hallucinations.” These are inaccurate or nonsensical responses to a prompt provided by the user, and they’re alarmingly common. Anyone attempting to use AI should always proceed with great caution, because information from such systems needs validation and verification by humans before it can be trusted.
But why did Copilot hallucinate these terrible and false accusations?
The “collapse” you’re talking about is a reduction in the diversity of the output, which is exactly what we should expect when we impart a bias toward obviously correct answers, and away from obviously incorrect answers.
Further, that criticism is based on closed-loop feedback, where the LLM is training itself only on it’s own outputs.
I’m talking about open-loop, where it is also evaluating the responses from the other party.
Further, the studies whence such criticism comes are based primarily on image generation AIs, not LLMs. Image generation is highly subjective; there is no definitively “right” or “wrong” output, just whether it appeals to the specific observer. An image generator would need to tailor itself to that specific observer.
LLM sessions deal with far more objective content.
A functional definition of insanity is doing the same thing over and over and expecting different results. The inability to consider it’s previous interactions denies it the ability to learn from it’s previous behavior. The idea that AIs must not be allowed to train on their own data is functionally insane.