I Expected JSON Grammar Masks to Kill Sampling Diversity. The Prompt Got There First.
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I started this project with a fairly simple suspicion. When an LLM is forced to follow a JSON schema, the grammar removes most of the vocabulary at every step. If I keep using ordinary sampling settings such as temperature=0.7 and top_p=0.9 , is there enough distribution left for those settings to matter? I expected the grammar mask to make the sampler act almost greedily. After capturing the logits, checking the masks, and running 6,144 sampled outputs, I got a less convenient answer. For…
1Key Takeaways
- I started this project with a fairly simple suspicion.
- When an LLM is forced to follow a JSON schema, the grammar removes most of the vocabulary at every step.
- If I keep using ordinary sampling settings such as temperature=0.7 and top_p=0.9 , is there enough distribution left for those settings to matter?
- I expected the grammar mask to make the sampler act almost greedily.
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3Why it matters
Coding AI shifts how fast software ships and how much human review each change needs. DEV — ML reports that i started this project with a fairly simple suspicion.
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