metadata
license: mit
task_categories:
- zero-shot-image-classification
N-COCO
This dataset is associated with the paper When Negation Is a Geometry Problem in Vision-Language Models.
N-COCO is a dataset designed to evaluate and improve negation understanding in Vision-Language Models (VLMs) like CLIP. The work identifies that a "negation direction" exists in the CLIP embedding space and can be manipulated via representation engineering to improve performance without fine-tuning.
- Repository: https://github.com/fawazsammani/negation-steering
- Paper: When Negation Is a Geometry Problem in Vision-Language Models
Dataset Description
The dataset includes the following files used for training and evaluation:
train_data.json: Contains positive-negative sentence pairs used to train layerwise steering directions.simpleneg.json: An evaluation benchmark for negation understanding.
Citation
If you find this work or dataset useful, please consider citing:
@misc{sammani2026negationgeometry,
title={When Negation Is a Geometry Problem in Vision-Language Models},
author={Fawaz Sammani and Tzoulio Chamiti and Paul Gavrikov and Nikos Deligiannis},
year={2026},
eprint={2603.20554},
archivePrefix={arXiv},
primaryClass={cs.CV}
}