My language model produces fluent responses.
Even when it does not know the answer, it sounds confident.
Users sometimes trust incorrect replies.
There is no indication of uncertainty.
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My language model produces fluent responses.
Even when it does not know the answer, it sounds confident.
Users sometimes trust incorrect replies.
There is no indication of uncertainty.
This happens because language models are trained to produce likely text, not to measure truth or confidence. They generate what sounds plausible based on training patterns.
Since the model does not have a built-in uncertainty estimate, it always outputs the most probable sequence, even when that probability is low. This makes wrong answers sound just as confident as correct ones.
Adding confidence estimation, retrieval-based grounding, or user-visible uncertainty thresholds helps reduce this risk.