One of the more interesting limitations in current LLMs is how confidently they can present incorrect information. In many cases, the delivery style, structure, and fluency of a response make it difficult to distinguish between: strong reasoning probabilistic guessing and outright hallucination What’s interesting is that capability has improved significantly across reasoning and benchmark performance, yet calibration still seems inconsistent in real-world use. Is this fundamentally a byproduct of next-token prediction architectures, or is confidence calibration something that can realistically be solved through training, retrieval systems, or model design changes? I’m also curious whether people think future systems should explicitly expose uncertainty more often instead of optimizing for conversational smoothness. submitted by /u/NoFilterGPT
Originally posted by u/NoFilterGPT on r/ArtificialInteligence