It depends if they are using a “vanilla” instruction-tuned model or are applying additional task-specific fine-tuning. Fine-tuning with data that doesn’t have misspellings can make the model “forget” how to handle them.
In general, fine-tuned models often fail to generalize well on inputs that aren’t very close to examples in the fine-tuning data set.
In general, fine-tuned models often fail to generalize well on inputs that aren’t very close to examples in the fine-tuning data set.