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No AI extraction system can be made incapable of hallucinating just by requiring JSON. A schema-constrained model can produce output that has the right shape while still inventing, misreading, or incorrectly inferring a value. Reliable extraction therefore needs two separate controls: enforce the output structure, then check every value against the source.
What “can’t hallucinate” means for structured extraction
Structured data extraction turns documents—such as PDFs, forms, or reports—into records with named fields. A schema defines the permitted shape of those records: for example, which keys are required, what types their values must have, and which values are allowed.
That can make output more predictable, but it cannot make the contents true. A response may be valid JSON and satisfy its schema while containing an unsupported date, a wrong amount, or a value inferred from context that the document never states. Syntactic validity and semantic fidelity are different properties, as Mujtaba Hasan’s 2026 ACL workshop paper, StructHallu-Drift, emphasizes.
It is more accurate to treat “can’t hallucinate” as an engineering goal: reduce unsupported values, make failures detectable, and measure how often the system gets each field right. Do not treat format constraints as a guarantee of factual accuracy.
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Why valid output can still be wrong
Schema constraints check form, not source support
A constrained-output API can enforce supported schema requirements, such as key structure and value types. Those checks answer whether an output conforms to the defined format—not whether a value appears in the source document. OpenAI’s API documentation describes strict schema adherence while noting that strict mode supports only a subset of JSON Schema; supported features can change, so check the provider’s current documentation before relying on a particular constraint.
Broad schemas can make extraction harder
ExtractBench evaluated PDF-to-JSON extraction using 35 PDF documents, paired schemas, and human-annotated labels, covering 12,867 evaluatable fields. Its authors report that validity fell to 0% across the tested models for one 369-field financial-reporting schema. That is an extreme result for a particular benchmark and schema, not a prediction for every model or a smaller extraction task. It does show why performance should be tested on the schema you actually plan to use.
Implicit details are especially risky
A 2024 chemistry-procedure extraction study illustrates the gap between clean records and correct values. After heuristic repair, 9,963 of 10,000 model outputs were valid ORD records (99.6%); under the study’s strict measure, accuracy for ProductCompound messages was 71.3%. The authors linked many errors to implicit details, including calculated yields. Those figures describe that study’s chemistry task, not general AI extraction accuracy.
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A practical workflow for reducing unsupported values
1. Design the schema around the job
Include fields that downstream users need, not every conceivable detail. Broad schemas, nested structures, and arrays can increase extraction and evaluation difficulty. For each field, define its meaning, expected type, allowed values, and whether it is required. Be especially precise about similar concepts—for example, whether a date means the document date, an event date, or a reporting-period end.
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2. Define what to do when evidence is missing
Specify how the system should represent a field that is absent, ambiguous, or not stated. Depending on the schema and the consuming software, that may mean a permitted null, an explicit unknown state, or omitting an optional field. Make the representation part of the schema and downstream contract. Do not pressure the model to fill every field by guessing.
3. Ask for evidence alongside each value
Where practical, have the system return a source trace for each extracted value, such as a supporting passage, page number, table, or document location. A reviewer can use that trace to check whether the value is supported and interpreted correctly. A citation or span generated by the model is an audit aid, not proof: it may point to irrelevant text or misrepresent what the passage says.
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4. Validate structure deterministically
Run a JSON parser and schema validator after generation. Check syntax, required keys, types, and allowed values; reject or route nonconforming records for repair or review. This catches structural defects. It does not establish that a value is present in the document or is correct.
5. Score extraction field by field
Build a representative test set with reference records checked by people who can interpret the documents. Use comparison rules suited to each field: exact comparison for identifiers, numeric comparison where appropriate, and carefully defined semantic comparison for text. Track omissions, unsupported additions, and incorrect values separately rather than combining them into one pass rate.
The open-source FAIRmat-NFDI JSON Extract Eval project supports field-specific comparators and reports precision, recall, F1, omissions, hallucinations, and mismatches. JSONSchemaBench likewise treats constrained decoding as more than a formatting problem, examining constraint compliance, schema coverage, and output quality. These approaches help distinguish “valid” from “useful and faithful.”
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6. Test the conditions that can break the pipeline
Run the same documents, schema, and scoring rules against candidate models or configurations. Include difficult layouts, tables, scans if they are in scope, nested arrays, schema changes, and fields likely to require inference. Review consequential errors with domain experts. Re-test after changing the schema, model, prompt, or document-processing path; a result on one configuration does not establish performance on another.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to compare extraction systems
Compare systems on the same target documents and schema, and keep structural compliance separate from semantic accuracy. Useful evaluation questions include:
| Dimension | What to check |
|---|---|
| Schema support | Which constraints and schema features are supported, and whether unsupported features are rejected or ignored. |
| Field accuracy | Per-field correctness on representative documents, including the treatment of wrong values, omissions, and unsupported additions. |
| Abstention behavior | How absent, ambiguous, or unstated information is represented—and whether the system guesses to fill required fields. |
| Evidence traceability | Whether values can be checked against useful source locations, with human verification rather than blind trust in the trace. |
| Document and schema complexity | Performance on the layouts, tables, arrays, nested structures, and schema breadth found in the real task. |
| Evaluation quality | How reference labels were checked, which field-level metrics are reported, and whether errors are categorized clearly. |
Operational considerations also matter: privacy terms, throughput, cost, and the need for human review depend on the provider and deployment. The cited benchmark and evaluation sources do not establish current comparative pricing or privacy terms; verify those directly in the chosen provider’s current documentation.
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What benchmark results do—and do not—tell you
Hasan’s July 2026 ACL Anthology workshop paper reports that 39–54% of structured outputs contained at least one semantic hallucination across 1,200 schema–model evaluation instances. This is a result from that study’s evaluation setting, not a universal rate for deployed systems. It is evidence that schema-constrained output does not eliminate semantic errors, not a forecast of a particular product’s performance.
Likewise, the ExtractBench result for a 369-field schema and the chemistry study’s accuracy figures are tied to their respective documents, schemas, models, and scoring methods. Use published studies to understand failure modes and motivate testing; use representative, human-checked examples from your own task to estimate how a pipeline performs for your use case.
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