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Constrained Decoding Eliminates Structural Failures in Small LLMs but Reveals a Scale-Dependent Semantic Gap
arXiv cs.CL — Computation and Language
Factual evidence
What the source reports
Benchmark shows constrained decoding fixes JSON structure in 0.6B–4B LLMs but reveals semantic errors at smaller scales.
Inspect the evidence
- Inclusion basis
- Enterprise AI
- Publisher and source type
- arXiv cs.CL — Computation and Language · RESEARCH
- Published by source
- 22 September 2026
- Collected by OneBench
- 23 Sept 2026, 03:01 UK
Stored source excerpt
arXiv:2609.23742v1 Announce Type: new Abstract: Small open-source large language models (LLMs) in the 0.6B-4B parameter range are increasingly deployed for structured output generation (JSON, function…
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