Update README.md

README.md

@@ -22,7 +22,7 @@ size_categories:
 ---
 
 # The StoryFrames Dataset
-StoryFrames is a human-annotated dataset created to enhance a model's capability of understanding and reasoning over sequences of images.
+[StoryFrames](https://arxiv.org/abs/2502.19409) is a human-annotated dataset created to enhance a model's capability of understanding and reasoning over sequences of images.
 It is specifically designed for tasks like generating a description for the next scene in a story based on previous visual and textual information.
 The dataset repurposes the [StoryBench dataset](https://arxiv.org/abs/2308.11606), a video dataset originally designed to predict future frames of a video.
 StoryFrames subsamples frames from those videos and pairs them with annotations for the task of _next-description prediction_.
@@ -121,4 +121,23 @@ Each story is composed of multiple scenes, where each scene is a part of the ove
 * `sentence_parts_nocontext`
   * Type: `List[str]`
   * A variant of the scene descriptions that excludes sequential context.
-  * This may be empty if no annotation was provided.
+  * This may be empty if no annotation was provided.
+
+## Citation
+The dataset was introduced as part of the following paper:
+
+[ImageChain: Advancing Sequential Image-to-Text Reasoning in Multimodal Large Language Models](https://arxiv.org/abs/2502.19409)
+
+If you use it in your research or applications, please cite the following paper:
+
+```
+@misc{villegas2025imagechainadvancingsequentialimagetotext,
+      title={ImageChain: Advancing Sequential Image-to-Text Reasoning in Multimodal Large Language Models}, 
+      author={Danae Sánchez Villegas and Ingo Ziegler and Desmond Elliott},
+      year={2025},
+      eprint={2502.19409},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2502.19409}, 
+}
+```