Datasets:

sulmo
/

FACTOR-Bench

license: cc-by-4.0
language:
  - en
pretty_name: FACTOR-Bench
size_categories:
  - 1K<n<10K
tags:
  - benchmark
  - vision-language
  - clip
  - compositionality
  - negation
  - boolean-operators
  - image-text-matching
configs:
  - config_name: default
    data_files:
      - split: test
        path: factor_bench.jsonl

FACTOR-Bench

A diagnostic benchmark that measures whether a vision-language scoring interface executes Boolean operators (negation, conjunction, disjunction, exclusion, NOR) or merely tracks which concepts are mentioned. It contains 1,695 image/caption-pair samples built from COCO val2017, all OWL-ViT-validated.

Introduced in Similarity Is Not Logic: Factored Inference for Dual-Encoder Vision-Language Models (ICML 2026).

Project page: https://sultanmo.github.io/factored-vlm/
Evaluation harness and reference results: https://github.com/SultanMo/factored-vlm

Usage

from datasets import load_dataset
ds = load_dataset("sulmo/FACTOR-Bench", split="test")

Each sample is a two-alternative forced choice: one image and two captions that share the same concepts but differ in logical structure. Score both captions and pick the higher; correct gives the answer.

{
  "sample_id": "not_explicit_0000",
  "test_type": "operator",
  "operator": "NOT",
  "image_id": 233825,
  "image": "val2017/000000233825.jpg",
  "caption_a": "an orange",
  "caption_b": "there is no orange",
  "correct": "b",
  "parse_a": {"concepts": [{"text": "orange", "is_negated": false}], "operator": "SINGLE"},
  "parse_b": {"concepts": [{"text": "orange", "is_negated": true}],  "operator": "SINGLE"},
  "meta": {"difficulty": "easy", "position_swapped": true}
}

Oracle parses are embedded in every sample, so evaluation does not depend on any text parser. Filter by test_type for the three splits:

operator (1,100): NOT / AND / OR / BUT_NOT / NEITHER. The main accuracy metric, chance = 50%.
equivalence (450): De Morgan / double negation / commutativity, correct="both". Metric = score-consistency violation rate.
compound (145): 3-4 concepts, mixed polarity.

Images

The dataset distributes captions, labels, parses, and COCO image IDs, not the images themselves. Download COCO val2017 (~1 GB) and resolve the image field against it:

wget http://images.cocodataset.org/zips/val2017.zip && unzip val2017.zip -d coco

Anti-shortcut design

Balanced answer positions (a/b about 50/50), balanced polarity on negation (about half the correct answers are the negated caption), 5+ template phrasings per operator, and OWL-ViT validation of every concept presence and absence.

License

Annotations: CC-BY-4.0. Images: COCO val2017 under its original terms (not redistributed). See the LICENSE file.

Citation

@inproceedings{alshehri2026similarity,
  title     = {Similarity Is Not Logic: Factored Inference for Dual-Encoder Vision-Language Models},
  author    = {Alshehri, Sultan and Yang, Zhantao and Zhang, Han and Savvides, Marios},
  booktitle = {International Conference on Machine Learning (ICML)},
  year      = {2026}
}