Datasets:

danielnichiata
/

citation-verification-benchmark

license: cc-by-4.0
language:
  - en
  - pt
  - es
  - de
pretty_name: CiteMe Citation Verification Benchmark v1
size_categories:
  - n<1K
tags:
  - citation-verification
  - citation-hallucination
  - fabricated-references
  - academic-integrity
  - multilingual
  - text
configs:
  - config_name: default
    data_files:
      - split: test
        path: corpus-v1.jsonl

CiteMe Citation Verification Benchmark v1

A frozen multilingual benchmark for evaluating citation- and reference-verification systems. It contains 340 labeled references with ground truth known by construction:

Ground-truth class	Count	Construction
`EXISTS_CORRECT`	160	Real works confirmed by DOI and title in OpenAlex and Crossref
`EXISTS_CORRUPTED`	100	Real works with exactly one controlled metadata corruption
`FABRICATED`	80	Constructed references whose invented titles had no close OpenAlex or Crossref match at build time

The corpus contains no user submissions, bibliographies, documents, product analytics, or other user data.

Canonical landing page: https://citeme.app/datasets/citation-verification-benchmark

Files

corpus-v1.jsonl — 116 evaluation cases containing 340 labeled references.
corpus-v1.meta.json — build timestamp, seed, counts, and discarded candidates.
SHA256SUMS.txt — integrity hashes for the frozen corpus and metadata.
CITATION.cff — machine-readable citation metadata.
LICENSE.md — dataset license and source-attribution notes.

Data structure

Each JSONL row is one evaluation case:

{
  "case_id": "case-en-001",
  "shape": "single",
  "style": "harvard",
  "sloppy": false,
  "language": "en",
  "refs": [
    {
      "ref_id": "case-en-001-r1",
      "text": "Reference string presented to the system",
      "class": "EXISTS_CORRUPTED",
      "sub_label": "title",
      "seed_doi": "10.xxxx/example"
    }
  ]
}

sub_label is present only for corrupted references and identifies the one changed field: author, doi, journal, pages_volume, title, or year.

Coverage

Languages: English (170 references), Portuguese (102), Spanish (51), German (17).
Citation styles: APA (146), Vancouver (90), Harvard (66), MLA (24), ABNT (14).
Input shapes: 102 single-reference cases and 14 bibliography bundles.
Twenty-three cases include deliberately untidy hand-typed formatting.

Construction and provenance

Correct references start from public OpenAlex metadata. Candidate DOI/title pairs are confirmed against Crossref before inclusion. Corrupted references receive exactly one deterministic mutation. Fabricated references are constructed from synthetic titles and public bibliographic strings, then rejected if OpenAlex or Crossref returns a close title match at build time.

The build seed is 20260723. The seed makes sampling order and synthetic transformations deterministic for a fixed upstream snapshot. Because OpenAlex and Crossref evolve, the frozen files and their SHA-256 checksums—not a future API rerun—are the reproducibility anchor for v1.

Source acknowledgements:

OpenAlex: https://openalex.org — source data is made available under CC0.
Crossref: https://www.crossref.org — public scholarly metadata used for DOI and title confirmation.

Synthetic-data notice

Fabricated rows may combine public contributor and venue strings with invented titles. They are synthetic negative test cases. They must never be interpreted as claims that a named person authored, or a venue published, the constructed work.

Limitations

This is an evaluation corpus, not a prevalence sample of academic writing.
Non-existence checks describe the OpenAlex/Crossref snapshot at build time.
The 29 separately curated legitimate but unindexed works used in CiteMe's 369-reference product evaluation are not part of corpus-v1.
Some constructed references intentionally resemble realistic hand-typed errors.
Future corrections or additions will receive a new version. Do not edit v1 in place.

Load with Hugging Face Datasets

On the Hub this corpus is exposed as the test split: it is an evaluation set, so there is deliberately no training split.

from datasets import load_dataset

# From the Hub (uses the split declared in this card's metadata)
data = load_dataset("danielnichiata/citation-verification-benchmark", split="test")

Loading the frozen file directly works too. data_files splits are named by the caller, so the name here is arbitrary and does not have to match the Hub split:

data = load_dataset(
    "json",
    data_files="https://citeme.app/datasets/citation-verification-benchmark-v1/corpus-v1.jsonl",
    split="train",
)

License

The CiteMe compilation, labels, and synthetic transformations are licensed under Creative Commons Attribution 4.0 International. See LICENSE.md.

Citation

CiteMe. (2026). CiteMe Citation Verification Benchmark v1
(Version 1.0.0) [Data set]. Zenodo.
https://doi.org/10.5281/zenodo.21610297

The DOI above is the version DOI for v1 and resolves once the Zenodo record is published. Canonical page: https://citeme.app/datasets/citation-verification-benchmark