Update README.md

README.md

@@ -155,14 +155,16 @@ The data has class imbalanced on both training and testing data splits, so we us
 #### Summary
 Table above presents the weighted F1 scores for predicting writing intentions across baselines and fine-tuned models. All models finetuned on ScholaWrite show a improvement performance compared to their baselines. BERT and RoBERTa achieved the most improvement, while LLama-8B-Instruct showed a modest improvement after fine-tuning. Those results demonstrate the effectiveness of our ScholaWrite dataset to align language models with writers' intentions. 
 
-## Citation
+## BibTeX
 
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
+```
+@misc{wang2025scholawritedatasetendtoendscholarly,
+      title={ScholaWrite: A Dataset of End-to-End Scholarly Writing Process},
+      author={Linghe Wang and Minhwa Lee and Ross Volkov and Luan Tuyen Chau and Dongyeop Kang},
+      year={2025},
+      eprint={2502.02904},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2502.02904},
+      }
+```