Update README.md

README.md

@@ -2,11 +2,14 @@
 license: apache-2.0
 ---
 
-### Generating Questions Given Context and Answers
+> This Question Generation model is a part of the [PlainQAFact](https://github.com/zhiwenyou103/PlainQAFact) factuality evaluation framework. 
+
+
+## Generating Questions Given Context and Answers
 
 Traditional BART model is not pre-trained on QG tasks. We fine-tuned `facebook/bart-large` model using 55k human-created question answering pairs with contexts collected by [Demszky et al. (2018)](https://arxiv.org/abs/1809.02922). The dataset includes SQuAD and QA2D question answering pairs associated with contexts.  
 
-### How to use
+## How to use
 Here is how to use this model in PyTorch:
 ```python
 from transformers import BartForConditionalGeneration, BartTokenizer
@@ -46,4 +49,19 @@ for i, question in enumerate(generated_questions, 1):
     print(f"Generated Question {i}: {question}")
 ```
 
-Adjusting parameter `num_return_sequences` to generate multiple questions.
+Adjusting parameter `num_return_sequences` to generate multiple questions.
+
+
+## Citation
+If you use data from PlainFact or PlainFact-summary, please cite with the following BibTex entry:
+```
+@misc{you2025plainqafactautomaticfactualityevaluation,
+      title={PlainQAFact: Automatic Factuality Evaluation Metric for Biomedical Plain Language Summaries Generation}, 
+      author={Zhiwen You and Yue Guo},
+      year={2025},
+      eprint={2503.08890},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2503.08890}, 
+}
+```