Update README.md

README.md

@@ -4,7 +4,7 @@ emoji: 🌖
 colorFrom: pink
 colorTo: indigo
 sdk: gradio
-sdk_version: 4.44.1
+sdk_version: 5.8.0
 app_file: app.py
 pinned: false
 license: mit
@@ -12,3 +12,37 @@ python_version: 3.8
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+
+## Citation
+
+<!-- https://arxiv.org/abs/2405.03595 -->
+
+**BibTeX:**
+```
+@inproceedings{ostmeier-etal-2024-green,
+    title = "{GREEN}: Generative Radiology Report Evaluation and Error Notation",
+    author = "Ostmeier, Sophie  and
+      Xu, Justin  and
+      Chen, Zhihong  and
+      Varma, Maya  and
+      Blankemeier, Louis  and
+      Bluethgen, Christian  and
+      Md, Arne Edward Michalson  and
+      Moseley, Michael  and
+      Langlotz, Curtis  and
+      Chaudhari, Akshay S  and
+      Delbrouck, Jean-Benoit",
+    editor = "Al-Onaizan, Yaser  and
+      Bansal, Mohit  and
+      Chen, Yun-Nung",
+    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
+    month = nov,
+    year = "2024",
+    address = "Miami, Florida, USA",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2024.findings-emnlp.21",
+    doi = "10.18653/v1/2024.findings-emnlp.21",
+    pages = "374--390",
+    abstract = "Evaluating radiology reports is a challenging problem as factual correctness is extremely important due to its medical nature. Existing automatic evaluation metrics either suffer from failing to consider factual correctness (e.g., BLEU and ROUGE) or are limited in their interpretability (e.g., F1CheXpert and F1RadGraph). In this paper, we introduce GREEN (Generative Radiology Report Evaluation and Error Notation), a radiology report generation metric that leverages the natural language understanding of language models to identify and explain clinically significant errors in candidate reports, both quantitatively and qualitatively. Compared to current metrics, GREEN offers: 1) a score aligned with expert preferences, 2) human interpretable explanations of clinically significant errors, enabling feedback loops with end-users, and 3) a lightweight open-source method that reaches the performance of commercial counterparts. We validate our GREEN metric by comparing it to GPT-4, as well as to error counts of 6 experts and preferences of 2 experts. Our method demonstrates not only higher correlation with expert error counts, but simultaneously higher alignment with expert preferences when compared to previous approaches.",
+}
+```