citations and links

README.md

@@ -20,6 +20,10 @@ Recent VLMs have demonstrated impressive capabilities, yet their ability to infe
 NL-Eye adapts the **abductive Natural Language Inference (NLI)** task to the visual domain, requiring models to evaluate the **plausibility of hypothesis images** based on a premise image and explain their decisions. The dataset contains **350 carefully curated triplet examples** (1,050 images) spanning diverse reasoning categories, temporal categories and domains.
 NL-Eye represents a crucial step toward developing **VLMs capable of robust multimodal reasoning** for real-world applications, such as accident-prevention bots and generated video verification.
 
+project page: [NL-Eye project page](https://venturamor.github.io/NLEye/)
+
+preprint: [NL-Eye arxiv](https://arxiv.org/abs/2410.02613)
+
 ---
 
 ## **Dataset Structure**
@@ -53,3 +57,15 @@ The `images/` directory contains **subdirectories named after each `sample_id`**
 
 ## **Usage**
 This dataset is **only for test purposes**. 
+
+### Citation
+```bibtex
+@misc{ventura2024nleye,
+  title={NL-Eye: Abductive NLI for Images},
+  author={Mor Ventura and Michael Toker and Nitay Calderon and Zorik Gekhman and Yonatan Bitton and Roi Reichart},
+  year={2024},
+  eprint={2410.02613},
+  archivePrefix={arXiv},
+  primaryClass={cs.CV}
+}
+