alecocc/mmscifact-dev

MMSciFact

Multimodal scientific fact-checking benchmark: model-generated question-answer pairs over scientific PDFs, with every answer sentence human-annotated for role (Observation/Interpretation), dependency structure (depends_on), and grounding label (supported/contradiction/NEI/not_a_claim) against the source document.

• 40 QA pairs across 6 papers, 440 annotated sentences.
• Every included QA pair has at least one contradiction/nei sentence (see src/utils/extract_final_annotations.py in the code repo).
• Error taxonomy (error_tags sentence-level for C4/N4 propagation, per-span tags in errors[] for C1-C3/N1-N3) and a mechanically-derived propagation_type per sentence (false_premise / unverifiable_premise / none) — see docs/guidelines.html in the code repo for the full taxonomy definitions.

Files

• mmscifact.jsonl — one row per QA pair.
• pdfs.zip — one PDF per paper (pdfs/<paper_id>.pdf), referenced by paper_pdf_path on every row. Unzip next to mmscifact.jsonl before running eval scripts: unzip pdfs.zip.

Using this with the MMSciFact eval scripts

mmscifact.jsonl is a flat, single-file view of the data -- convenient for browsing, but the eval scripts in the code repo (eval_batch_api.py, run_vllm_batch.py, eval_oracle_graph.py, eval_holistic_graph.py) all expect the original one-file-per-QA-pair layout (final_annotations/<paper_id>/<qa_pair_id>.json + _source.pdf). Restore that layout with src/utils/hydrate_from_hf.py from the code repo:

hf download alecocc/mmscifact-demo --repo-type dataset --local-dir hf_download
python src/utils/hydrate_from_hf.py --dataset-dir hf_download
# writes final_annotations/<paper_id>/... at the repo root -- every eval
# script then runs completely unmodified from there.

Row schema

Each line of mmscifact.jsonl is one QA pair.

Field	Type	Meaning
qa_pair_id	string	Unique identifier for this QA pair
paper_id	string	Join key — the PDF is at pdfs/<paper_id>.pdf after unzipping pdfs.zip
paper_pdf_path	string	Relative path to the source PDF, pdfs/<paper_id>.pdf
level	string	How much of the paper the question requires: single_page, cross_page, or full_paper
scenario	string	Question-generation scenario code (S2–S5) — see the paper's Appendix A
question_subtype	string	Finer-grained question type (e.g. comparison, trend, methodological)
question_model	string	Model that generated the question
answer_model	string	Model that generated the answer being fact-checked
question_text	string	The question text
question_meta	object	Extra generation-time metadata (input types, cognitive operation, visual/error subtype, scope)
answer_raw	string	The full model-generated answer, before it was split into sentences
annotations	list of objects	Per-sentence human annotation — see below
source_images	list of strings	Page-number-encoding paths for which PDF page(s) the answer draws on

Each entry of annotations:

Field	Type	Meaning
sentence	string	The sentence text (one segment of answer_raw)
label	string	Grounding verdict: supported, contradiction, nei, or not_a_claim
role	string | null	Observation (a direct, self-contained claim) or Interpretation (an inference drawn from other sentences)
rationale	string	Human-written explanation for the label
depends_on	list of strings	Sentence IDs this sentence's claim logically depends on, e.g. ["S2", "S3"]
error_tags	list of strings	Sentence-level propagation code (C4/N4), if this sentence's error is inherited from a bad premise elsewhere in the answer
propagation_type	string	Mechanically derived from label+depends_on: false_premise, unverifiable_premise, or none
errors	list of objects	Specific erroneous spans within the sentence — see below
2d_box	list of objects	Bounding box(es) anchoring this sentence to the source document — see below

Each entry of annotations[].errors:

Field	Type	Meaning
span	string	The exact phrase containing the error
tags	list of strings	Error code(s) for this span (C1–C3 for contradiction, N1–N3 for NEI)
correction	string	Corrected wording for the span; empty for NEI (nothing to replace with — the claim is unverifiable, not wrong)

Each entry of annotations[].2d_box:

Field	Type	Meaning
page_id	int	1-indexed PDF page number
page_name	string	Human-readable page label
image_id	string	Page image identifier
coords	[int, int, int, int]	Bounding box pixel coordinates [x1, y1, x2, y2]
img_size	[int, int]	[width, height] of the page image the coords are relative to

variant, target_label, question_valid, invalid_rationale, and source_pdf are omitted — unused downstream (question_valid is always true by construction: only pairs that passed question validation and were then annotated ever reach this dataset), and source_pdf was purely derivable from level == "full_paper".

License

Annotations (labels, rationale, error codes, dependency structure) are released under CC-BY-4.0. Source PDFs are from openly-accessible papers (NeurIPS, ICLR, ICCV, and similar venues); [TODO: confirm per-paper license terms before publishing -- this card asserts open accessibility, not a verified redistribution license for every included PDF].

Citation

[TODO: add citation once the paper has a venue/BibTeX entry.]