Improve dataset card with paper, code, and sample usage

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

I noticed this dataset card could benefit from more documentation. This PR adds:
- Links to the paper, project page, and GitHub repository.
- A brief description of the benchmark based on the abstract.
- A sample usage code snippet found in the official GitHub repository.
- The BibTeX citation for the paper.

These additions will make the dataset more discoverable and easier to use for the community.

README.md

@@ -1,4 +1,11 @@
 ---
+language:
+- en
+license: mit
+size_categories:
+- n<1K
+task_categories:
+- text-generation
 dataset_info:
   features:
   - name: query
@@ -36,11 +43,32 @@ configs:
   data_files:
   - split: train
     path: data/train-*
-license: mit
-task_categories:
-- text-generation
-language:
-- en
-size_categories:
-- n<1K
 ---
+
+# CREATE: Testing LLMs for Associative Creativity
+
+[**Project Page**](https://manyawadhwa.github.io/projects/create/) | [**Github**](https://github.com/ManyaWadhwa/CREATE) | [**Paper**](https://huggingface.co/papers/2603.09970)
+
+CREATE is a benchmark designed to evaluate models' capacity for creative associative reasoning: the ability to draw novel yet meaningful connections between concepts. It requires models to generate sets of paths connecting concepts in their parametric knowledge. Paths are evaluated based on specificity (distinctiveness and closeness of the connection) and diversity.
+
+## Sample Usage
+
+You can load the benchmark questions using the `datasets` library:
+
+```python
+from datasets import load_dataset
+
+data = load_dataset('wadhma/CREATE')['train'].to_pandas() 
+print(data['query']) ## the benchmark questions
+```
+
+## Citation
+
+```bibtex
+@InProceedings{Wadhwa-Et-Al-2026:CREATE,
+  title = {CREATE: Testing LLMs for Associative Creativity},
+  author = {Manya Wadhwa and Tiasa Singha Roy and Harvey Lederman and Junyi Jessy Li and Greg Durrett},
+  booktitle = {arXiv},
+  year = {2026},
+}
+```