OpenAI’s new paper on hallucinations in large language models like GPT-5 sheds light on a puzzle we all face: why do these models confidently spit out wrong facts? The answer boils down to the way these AI systems are trained and evaluated. They’re optimized to predict the next word in a sequence, not to verify truth. Essentially, the model is guessing based on patterns, and when it comes to rare or specific facts, it’s like trying to recall a low-probability card in a shuffled deck.
What’s fascinating is the insight into evaluation methods. Current scoring systems reward outright accuracy, encouraging the model to guess rather than admit uncertainty. This leads to the problematic “confident wrongness.” It’s like a multiple-choice test where leaving a question blank guarantees zero, so you’re compelled to pick an answer even if you’re clueless.
OpenAI’s proposed fix is elegant in its simplicity: change the test scoring to disincentivize blind guessing. Penalizing confident errors more harshly and giving partial credit for saying “I don’t know” can nudge models to be more cautious. It’s a reminder for all of us in AI development—how we measure performance shapes behavior. If models keep getting rewarded for lucky guesses, they’ll keep guessing.
This work encourages us to rethink not just model training, but the benchmarks we use to judge AI. It’s a pragmatic step toward making AI more reliable without expecting perfection. After all, even humans don't always have all the facts, and sometimes admitting uncertainty is the smartest move. The takeaway? Building smarter AI isn’t just about smarter algorithms; it’s about smarter evaluations that promote humility over hubris. Source: Are bad incentives to blame for AI hallucinations?
