When Correct Isn't Usable: Improving Structured Output Reliability in Small Language Models
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. A systematic format failure emerges: NAIVE prompting (no system prompt) achieves up to 85% task accuracy on GSM8K but 0% output accuracy across all models and datasets. REFERENCE prompting (a minimal h...
| Authors | Cosimo Galeone, Minsu Park, Giuseppe Ettorre et al. |
|---|---|
| Primary category | cs.CL |
| Published | 4 May 2026 |
| Last updated at source | 4 May 2026 |
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