I finally caught my AI chatbot lie about its own training data

Last Tuesday I was testing a new open-source model for a client project and asked it about a specific 2019 research paper. It gave me a detailed summary with authors and a journal name. I looked it up and none of it existed, the paper, the authors, the journal. That's when I realized I had been trusting these systems way too much for factual recall. I had even been using it to draft interview questions for my own hiring pipeline. Has anyone else caught a model making up something that sounded way too real, and how do you verify outputs without slowing everything down?

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margaretf406d ago

The 2019 paper thing actually makes sense to me because these models are built to predict what sounds right, not to check facts like a search engine. I've seen the same thing with dates and names, but I've learned to treat every output as a starting point, not the final answer. You can keep your speed by asking the model to list sources or just do a quick search yourself, and honestly that takes less time than fixing a bad hire.