A new approach uses language models to interrogate other language models and sniff out lies
If you ask an artificial intelligence system such as ChatGPT where the Eiffel Tower is, chances are the chatbot will correctly say “Paris.” But repeatedly ask that AI the same question, and you may eventually be told that no, actually, the answer is Rome. This mistake might seem trivial, but it signals a more serious issue plaguing generative AI: hallucination, or when AI creates content that’s unfaithful to reality.
Hallucinations have proved elusive and persistent, but computer scientists are refining ways to detect them in a large language model, or LLM . And now a new project aims to check an LLM’s output for suspected flubs—by running it throughLLM. This second AI system examines multiple answers from the first, assessing their consistency and determining the system’s level of uncertainty.
To pinpoint when a language model might be confabulating, the new method involves asking a question multiple times to produce several AI-generated answers. Then a second LLM groups these answers according to their meaning; for instance, “John drove his car to the store” and “John went to the store in his car” would be clustered together. This process is repeated for every generated answer.
Other antihallucination methods have used LLMs to evaluate generated answers, through approaches such as asking a single model to double-check its own work. But the paired system improves on this, distinguishing correct from incorrect answers with about 10 percent more accuracy, according to the new study.Still, the new process isn’t a flawless way to spot AI hallucinations.
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