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cloud.google.com·6h ago
Data Science
Towards Data Science·Sep 1·5 min read
Your JSON Is Valid but Your Data Is Wrong: Five Failure Modes LLM Structured Outputs Won't Catch
TL;DR
Large Language Models (LLMs) can produce valid JSON outputs, but they may still contain incorrect data due to five specific failure modes. Understanding these limitations is crucial for ensuring data integrity.
✦ Why It Matters
Implement additional validation checks beyond schema validation to ensure data accuracy in your applications.
Key Takeaways
- Monitor output entropy to detect distributional collapse.
- Audit outputs from ambiguous inputs to catch confident fabrication.
- Implement cross-field validation to avoid contradictions.
- Add uncertainty measures to express model confidence.
How It Works
Constrained decoding uses a finite-state machine to ensure valid token selection, producing JSON outputs that adhere to a defined schema. This method eliminates common syntax errors but can obscure deeper semantic issues.
âš The Catch
Schema validation only checks if data types are correct, not if the values make sense in context, leading to silent failures in data integrity.