The model produces grammatically correct text.
But it keeps repeating the same phrases.
The output never moves forward.
It feels stuck in a loop?
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The model produces grammatically correct text.
But it keeps repeating the same phrases.
The output never moves forward.
It feels stuck in a loop?
This happens when decoding is too greedy and the probability distribution collapses. The model finds one safe high-probability phrase and keeps choosing it.
Using temperature scaling, top-k or nucleus sampling introduces controlled randomness so the model explores alternative paths.
Common mistakes:
Using greedy decoding
No sampling strategy
Overconfident probability outputs
The practical takeaway is that generation quality depends heavily on decoding strategy.