It’s not surprising that there could be a very slight quality drop off for making the model return its answer in a constrained way. You’re essentially forcing the model to express the actual answer it wants to express in a constrained language.
However I would say two things: 1. I doubt this quality drop couldn’t be mitigated by first letting the model answer in its regular language and then doing a second constrained step to convert that into structured outputs. 2. For the smaller models I have seen instances where the constrained sampling of structured outputs actually HELPS with output quality. If you can sufficiently encode information in the structure of the output it can help the model. It can effectively let you encode simple branching mechanisms to execute at sample time
You surely aren't implying that the model is sentient or has any "desire" to give an answer, right?
And how is that different from prompting in general? Isn't using english already a constraint? And isn't that what it is designed for, to work with prompts that provide limits in which to determine the output text? Like there is no "real" answer that you supress by changing your prompt.
So I don't think its a plausible explanation to say this happens because we are "making" the model return its answerr in a "constrained language" at all.
The model is a probabilistic machine that was trained to generate completions and then fine tuned to generate chat style interactions. There is an output, given the prompt and weights, that is most likely under the model. That’s what one could call the model’s “desired” answer if you want to anthropomorphize. When you constrain which tokens can be sampled at a given timestep you by definition diverge from that