SOURCE-LINKED INTELLIGENCE
Where vs What: Decomposing Structural and Content Failures in LLM-Generated Structured Outputs
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
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-08-26T04:32:06.000Z
First collected: 2026-09-21T09:42:05.193Z. This is not the publication date.