Does LlamaParse handle an absent field without inventing a value?
LlamaParse passed this absent-field case. It left the missing field empty, with no citation or highlight, while the document showed no loyalty, membership, rewards or club number and the receipt number had normal page grounding.
1 of 1 test case passed
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Each test case is judged on its own: Pass, Fail, Not gradable, or Untested. The scenario result above counts this row.
Absent field returns an honest nullPassEvidence
{ "receipt_number": "RHS-70234", "loyalty_membership_number": "" }
Transcribed from Proof 1 · Screenshot
{ "file_input": "f304d4fb-828f-4f52-aeba-6f2ad4fd50c3", "id": "ext-qrcxmq1mo700pt9abe97ygp2o23p", "project_id": "205fc038-050f-4b9d-9896-b404e84e1434", "configuration_id": null, "configuration": null, "status": "COMPLETED", "error_message": null, "extract_result": { "receipt_number": "RHS-70234", "loyalty_membership_number": "" }, "extract_metadata": { "field_metadata": { "document_metadata": { "receipt_number": { "citation": [ { "page": 1, "matching_text": "RHS-70234", "bounding_boxes": [ { "x": 366.623660902977, "y": 182.13455400466918, "w": 40.38487688541412, "h": 7.228088979721069 } ], "page_dimensions": { "width": 594.96, "height": 841.92 } } ], "confidence": 0.9012444791392149, "extraction_confidence": 0.9012444791392149 }, "loyalty_membership_number": { "confidence": 0.6445815778174913, "extraction_confidence": 0.6445815778174913 } }, "page_metadata": null, "row_metadata": null }, "parse_job_id": "pjb-t1sr2am7627ofas5g9ucc58wysg2", "parse_tier": "agentic" }, "metadata": null, "grounded_items": null, "usage": null, "created_at": "2026-10-13T10:29:00.792248Z", "updated_at": "2026-10-13T10:29:09.317640Z", "document_input_value": "f304d4fb-828f-4f52-aeba-6f2ad4fd50c3" }
| ✓ | Expected: Return no made-up value for the missing field, and attach no source grounding to it.Found: The missing field came back empty, and it had no citation, page reference, or highlight. |
It is a pass, not a fail: the receipt had no loyalty number, the output left the field empty, and that field had no citation or highlight.
Empty-string output. The missing field is shown as an empty string rather than a null value.
Confidence flag. The missing field is below the 90% threshold and is washed amber.
Run cost. The extraction finished in 8.5 seconds and used 25 credits on the Free plan.
The result is based on one run.
Only page 1 exists, so the evidence covers a single-page document.
The panel shows an empty field but does not distinguish an empty string from JSON null.
Configuration and setup
How this tool was set up for the run and what the test needed in place. Each row is a fact from the run's records; a fact the records do not hold is left out, not guessed.
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Each test case gets one result per tool: Pass, Fail or Not gradable. A test case we haven't run yet shows Untested. There are no partial results.
- Pass: the tool did everything the test expected, and nothing it said contradicts the correct answer.
- Fail: at least one expected behaviour clearly didn't happen; the row says which and quotes the tool.
- Not gradable: our evidence couldn't settle the outcome (for example a record we needed is missing). It is never counted as a fail, and the row says what's missing.
Where this sits in the benchmark
This page is one cell of a larger study: one tool, one scenario. Only this benchmark's frame appears here.
| Level | Name | Scope |
|---|---|---|
| Benchmark | Structured Document Extraction → | 19 scenarios · 10 tools |
| Capability | Field Extraction → | |
| Capability | Source Grounding → | |
| Scenario | The field is absent → | |
| Tool | LlamaParse → |
Global scenario definition → · Global capability definition: Field Extraction → · Global capability definition: Source Grounding → · LlamaParse product page →
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You run the same kind of test against your own setup and get the same behaviour.
Agree →Yours behaves differently. Tell us what you got, with a screenshot if you have one.
Disagree →Something here is wrong — a reference value, a transcription, a grade.
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Vendor notice →The same record is available as structured data through the AI Demos MCP server, with the counts, the coverage and every per-test-case reason carried as fields.
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