I also learned about constrainted decoding[1], that they give a brief explanation about. This is a really clever technique! It will increase reliability as well as reduce latency (less tokens to pick from) once the initial artifacts are loaded.
If your schema is not supported, but you still want to use the model to generate output, you would use `strict: false`. Unfortunately we cannot make `strict: true` the default because it would break existing users. We hope to make it the default in a future API version.
For example, if I ask an LLM to generate social security numbers, it will give the whole "I'm sorry Hal, I can't do that". If I ban all tokens except numbers and hyphens, prior to your "refusal = True" approach, it was guaranteed that even "aligned" models would generate what appeared to be social security numbers.
Christ, I hate the AI safety people who brain-damage models so that they refuse to do things trivial to do by other means. Is LLM censorship preventing bad actors from generating social security numbers? Obviously not. THEN WHY DOES DAMAGING AN LLM TO MAKE IT REFUSE THIS TASK MAKE CIVILIZATION BETTER OFF?
History will not be kind to safetyist luddites.
That said, when zero "safety" is at stake might be the best time to experiment with how to build and where to put safety latches, for when we get to a point we mean actual safety. I'm even OK with models that default to parental control for practice provided it can be switched off.
The wider consumer base is practically itching for a reason to avoid this tech. If it gets out, that could be a problem.
It was the same issue with Geminis image gen. Sure, the problem Google had was bad. But could you even imagine what it would've been if they did nothing? Meaning, the model could generate horrendously racist images? That 100% would've been a worse PR outcome. Imagine someone gens a mistral image and accredits it to Google's model. Like... that's bad for Google. Really bad.