Add citation information.

README.md

@@ -28,6 +28,8 @@ VS(K) = exp(tr(K/n @ log(K/n))) = exp(-sum_i lambda_i log lambda_i),
 where `lambda_i` are the eigenvalues of `K/n` and `0 log 0 = 0`.
 That is, the Vendi Score is equal to the exponential of the von Neumann entropy of `K/n`, or the Shannon entropy of the eigenvalues, which is also known as the effective rank.
 
+For more information, please see our paper, [The Vendi Score: A Diversity Evaluation Metric for Machine Learning](https://arxiv.org/abs/2210.02410).
+
 ## How to Use
 The Vendi Score is available as a Python package or in HuggingFace `evaluate`.
 To use the Python package, see the instructions at https://github.com/vertaix/Vendi-Score.
@@ -124,4 +126,11 @@ N-grams: 3.91, BERT: 1.21
 The Vendi Score depends on the choice of similarity function. Care should be taken to select a similarity function that reflects the features that are relevant for defining diversity in a given application.
 
 ## Citation
-
+```bibtex
+@article{friedman2022vendi,
+  title={The Vendi Score: A Diversity Evaluation Metric for Machine Learning},
+  author={Friedman, Dan and Dieng, Adji Bousso},
+  journal={arXiv preprint arXiv:2210.02410},
+  year={2022}
+}
+```