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README.md

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 # NYC Taxi Trip Description Dataset
 
-This dataset contains NYC taxi trip data from May 1-7, 2013, excluding trips to and from Staten Island. It includes 2,957 sequences with 362,374 events and 8 location types. The data can be downloaded from [NYC Taxi Trips](https://www.andresmh.com/nyctaxitrips/) and is subject to the [NYC Terms of Use](https://www.nyc.gov/home/terms-of-use.page). The detailed data preprocessing steps used to create this dataset can be found in this [paper](https://arxiv.org/abs/2410.02062).
+This dataset contains NYC taxi trip data from May 1-7, 2013, excluding trips to and from Staten Island. It includes 2,957 sequences with 362,374 events and 8 location types. The data can be downloaded from [NYC Taxi Trips](https://www.andresmh.com/nyctaxitrips/) and is subject to the [NYC Terms of Use](https://www.nyc.gov/home/terms-of-use.page). The detailed data preprocessing steps used to create this dataset can be found in the [TPP-LLM paper](https://arxiv.org/abs/2410.02062) and [TPP-LLM-Embedding paper](https://arxiv.org/abs/2410.14043).
+
+If you find this dataset useful, we kindly invite you to cite the following papers:
+```bibtex
+@article{liu2024tppllmm,
+  title={TPP-LLM: Modeling Temporal Point Processes by Efficiently Fine-Tuning Large Language Models},
+  author={Liu, Zefang and Quan, Yinzhu},
+  journal={arXiv preprint arXiv:2410.02062},
+  year={2024}
+}
+
+@article{liu2024efficient,
+  title={Efficient Retrieval of Temporal Event Sequences from Textual Descriptions},
+  author={Liu, Zefang and Quan, Yinzhu},
+  journal={arXiv preprint arXiv:2410.14043},
+  year={2024}
+}
+```