This makes sense. Low semantic entropy probably means the answer was more represented in the unsupervised learning training data, or in later tuning. And I unde

This makes sense. Low semantic entropy probably means the answer was more represented in the unsupervised learning training data, or in later tuning. And I understand this is a tool to indirectly measure how much it was represented? It’s an interesting idea to measure certainty this way. The problem remains that the model can be certain in this way and wrong. But the author did say this was a partial solution. Still, wouldn’t we be able to already produce a confidence score at the model level like this? Instead of a “post-processor”?