Where vs What: Decomposing Structural and Content Failures in LLM-Generated Structured Outputs

arXiv cs.AIAugust 27, 2026en

arXiv cs.AI

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arXiv:2608.25358v1 Announce Type: new Abstract: Structured outputs such as JSON and tables are central to modern LLM-based systems, yet generation failures are evaluated monolithically, conflating two distinct error modes: placement errors (correct values at wrong positions) and value errors (wrong values at intended positions). We introduce Structure-Content Decomposition (SCD), a framework that independently measures structural fidelity and content accuracy. Applying SCD to nested JSON and table tasks across six models (7B to frontier), we uncover a consistent phenomenon: structural fidelity degrades earlier and more sharply than content accuracy as complexity increases. At the highest com

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