AI RESEARCH
When Correct Isn't Usable: Improving Structured Output Reliability in Small Language Models
arXiv CS.LG
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ArXi:2605.02363v1 Announce Type: cross Deployed language models must produce outputs that are both correct and format-compliant. We study this structured-output reliability gap using two mathematical benchmarks -- GSM8K and MATH -- as a controlled testbed: ground truth is unambiguous and the output contract is strict (JSON with required fields). We evaluate three 7-9B models under five prompting strategies and report output accuracy -- the joint event of mathematical correctness and valid JSON structure -- as the primary metric.