Update citing

README.md

@@ -80,4 +80,15 @@ Each example has the following fields:
 
 To compare the predicted documentation and the ground truth documentation, we introduce the new metric based on LLM as an assessor. Our approach involves feeding the LLM with relevant code and two versions of documentation: the ground truth and the model-generated text. The LLM evaluates which documentation better explains and fits the code. To mitigate variance and potential ordering effects in model responses, we calculate the probability that the generated documentation is superior by averaging the results of two queries with the different order.
 
-For more details about metric implementation, please refer to [our GitHub repository](https://github.com/JetBrains-Research/lca-baselines).
+For more details about metric implementation, please refer to [our GitHub repository](https://github.com/JetBrains-Research/lca-baselines).
+
+## Citing
+```
+@article{bogomolov2024long,
+  title={Long Code Arena: a Set of Benchmarks for Long-Context Code Models},
+  author={Bogomolov, Egor and Eliseeva, Aleksandra and Galimzyanov, Timur and Glukhov, Evgeniy and Shapkin, Anton and Tigina, Maria and Golubev, Yaroslav and Kovrigin, Alexander and van Deursen, Arie and Izadi, Maliheh and Bryksin, Timofey},
+  journal={arXiv preprint arXiv:2406.11612},
+  year={2024}
+}
+```
+You can find the paper [here](https://arxiv.org/abs/2406.11612).