We were messing around at work last week building an AI agent that was supposed to only respond with JSON data. GPT and Sonnet more or less what we wanted, but Gemma insisted on giving us a Python code snippet.
Whom's messenger? You didn't point us to anyone's research.
I just don't see how sampling tokens constrained to a grammar can be worse than rejection-sampling whole answers against the same grammar. The latter needs to follow the same constraints naturally to not get rejected, and both can iterate in natural language before starting their structured answer.
Under a fair comparison, I'd expect the former to provide answers at least just as good while being more efficient. Possibly better if top-whatever selection happened after the grammar constraint.
I will die on this hill and I have a bunch of other Arxiv links from better peer reviewed sources than yours to back my claim up (i.e. NeurIPS caliber papers with more citations than yours claiming it does harm the outputs)
Any actual impact of structured/constrained generation on the outputs is a SAMPLER problem, and you can fix what little impact may exist with things like https://arxiv.org/abs/2410.01103
