VerifyDocBench

Genuine per-field trust records for document-extraction calibration, risk-controlled selective prediction, and grounding research. Companion resource to:

• VerifyDocBench (benchmark paper) — first released benchmark scoring per-field calibration, risk-controlled selective prediction, and grounding-conditioned trust for document extraction.
• "Valid Per-Field Selective Risk Control for Document Extraction" (method paper) — the validity ladder / conformal risk-control procedures this data was captured to evaluate.

Code, loaders, scoring harness, and both papers' reproducibility artifacts: https://github.com/bhaskargurram-ai/verifydoc (Apache-2.0).

What's in this repository

Nine genuine per-field capture files, 36,265 records total, from four extractor families over four corpora:

file	extractor	fields	docs	source corpus
cord_claude-sonnet-5.json	claude-sonnet-5 (text-layer)	13,859	800	CORD (receipts)
funsd_claude-sonnet-5.json	claude-sonnet-5 (text-layer)	1,999	175	FUNSD (forms)
xfund_de_claude-sonnet-5.json	claude-sonnet-5 (text-layer)	523	42	XFUND German
xfund_es_claude-sonnet-5.json	claude-sonnet-5 (text-layer)	632	43	XFUND Spanish
xfund_fr_claude-sonnet-5.json	claude-sonnet-5 (text-layer)	772	44	XFUND French
xfund_zh_claude-sonnet-5.json	claude-sonnet-5 (text-layer)	461	27	XFUND Chinese
cord_claude-haiku-4-5.json	claude-haiku-4-5 (text-layer)	5,341	400	CORD (frozen-config confirmation)
cord_qwen2.5-14b.json	Qwen2.5-14B (vLLM, open-weights, text-layer)	6,168	398	CORD
cord_gpt-4o.json	gpt-4o (genuinely vision-based)	6,510	399	CORD

Only cord_gpt-4o.json is genuinely vision-based — the model was sent the rendered page image (base64-encoded) alongside the OCR text layer as assistive context. Every other file is text-layer prompted: the model reads only the document's OCR text, never pixels, despite "VLM" being the natural shorthand for a closed frontier API model — we correct that framing here rather than let it stand for three of the four extractor families.

Each record is one field prediction:

{
  "doc_id": "cord-train-00000",
  "path": "menu.nm",
  "value": "Nasi Campur Bali",
  "verbalized": 0.95,
  "consistency": 1.0,
  "grounded": true,
  "support": 0.83,
  "correct": 1,
  "entailment": 0.91
}

verbalized = the model's self-reported confidence; consistency = k-sample self-consistency agreement; grounded/support = whether/how strongly the value was located in the document's text layer (ambiguity-penalized); entailment = NLI cross-encoder score for "does the grounded span entail this value"; correct = scored against gold (see the paper for exact-match/numeric/semantic scoring rules per field type).

Not included, and not claimed as released: DocILE and SROIE — no genuine capture exists for either in this project (DocILE download is pending a valid access token; SROIE has loader code but was never run for a real capture). Source document images are not re-hosted for any corpus (see licensing below) — only our own derived per-field predictions and scoring.

Licensing by source

Our own contributions — the per-field correctness labels, grounding/support scores, schemas, and this derived record format — are released under Apache-2.0, matching the parent repository's license.

This does not relicense the underlying source document collections, each of which keeps its own license:

source	license	notes
CORD	CC BY 4.0	Permissive; our Apache-2.0 annotations are consistent with it.
FUNSD	Non-commercial, research/educational use only (custom terms)	Not a CC license. Use of funsd_claude-sonnet-5.json must stay within these terms.
XFUND (de/es/fr/zh)	CC BY-NC-SA 4.0	Non-commercial, share-alike; uniform across languages.

We redistribute only our own added annotations (values, confidence/grounding/ correctness signals) and reference the original dataset downloads for source documents/images — restrictively licensed source images are never re-hosted here. If you redistribute derivatives of the FUNSD or XFUND slices, you must comply with their non-commercial terms.

Reproducibility

Captured via the harness at scripts/apivlm_perfield_rich.py (seed-pinned, k=3 self-consistency sampling, doc-level splits, regression-gated). Every number in both companion papers traces to these files or to the scoring harness's deterministic transforms of them — see paper/HANDOFF.md and paper/generated/real-runs/ in the GitHub repository for the full campaign log.

Human-gold audit

A subset of these records was independently re-judged by three blind human annotators (Fleiss' κ=0.83 on 600 CORD/FUNSD items, κ=0.939 on a further 400 XFUND items) to bound automatic-label noise. See the method paper's confirmation section and benchmark/card.md in the GitHub repository for the full audit.

Citation

@misc{verifydocbench2026,
  title  = {VerifyDocBench: Measuring Per-Field Calibration, Selective Risk, and Grounding for Document Extraction},
  author = {Gurram, Bhaskar},
  year   = {2026},
  url    = {https://github.com/bhaskargurram-ai/verifydoc}
}

Maintenance

Maintained by the repository owner via GitHub issues: https://github.com/bhaskargurram-ai/verifydoc/issues. No formal versioning/erratum policy beyond the issue tracker as of this release.