Add paper link, project page, and task category metadata

Hi! I'm Niels from the community science team at Hugging Face.

This PR improves the dataset card for HLVid by:
- Adding the `video-text-to-text` task category to the metadata.
- Providing links to the research paper ("Attend Before Attention: Efficient and Scalable Video Understanding via Autoregressive Gazing"), project page, and GitHub repository.
- Adding a description of the benchmark based on the paper abstract.
- Documenting the dataset features for better discoverability.

README.md

@@ -1,4 +1,6 @@
 ---
+task_categories:
+- video-text-to-text
 dataset_info:
   features:
   - name: question_id
@@ -23,3 +25,31 @@ configs:
   - split: test
     path: data/test-*
 ---
+
+# HLVid Dataset
+
+[Project Page](https://autogaze.github.io/) | [Paper](https://huggingface.co/papers/2603.12254) | [GitHub](https://github.com/NVlabs/AutoGaze)
+
+HLVid (High-resolution, Long-form Video QA) is a benchmark introduced in the paper "[Attend Before Attention: Efficient and Scalable Video Understanding via Autoregressive Gazing](https://huggingface.co/papers/2603.12254)". 
+
+It is designed to evaluate Multi-modal Large Language Models (MLLMs) on long-form, high-resolution video understanding. The benchmark features 5-minute videos at 4K resolution, challenging models to handle significant spatiotemporal redundancy while preserving critical information.
+
+## Dataset Details
+
+The dataset contains question-answering pairs based on high-fidelity video content. Each entry in the `test` split includes:
+
+- `question_id`: A unique identifier for the sample.
+- `category`: The specific domain or reasoning category of the video/question.
+- `video_path`: The path or reference to the source video file.
+- `question`: The text-based question regarding the video.
+- `answer`: The ground-truth text answer.
+
+### Citation
+```bibtex
+@article{shi2024autogaze,
+  title={Attend Before Attention: Efficient and Scalable Video Understanding via Autoregressive Gazing},
+  author={Shi, Baifeng and Fu, Stephanie and Lian, Long and Ye, Hanrong and Eigen, David and Reite, Aaron and Li, Boyi and Kautz, Jan and Han, Song and Chan, David M and others},
+  journal={arXiv preprint arXiv:2412.04452},
+  year={2024}
+}
+```