Update README.md

README.md

@@ -20,4 +20,31 @@ library_name: transformers
 For further details about Libra-v0.5, including its architecture, training process, and use cases, please refer to the following resources:
 - Article: Comprehensive paper describing Libra’s design and experiments [Gla-AI4BioMed at RRG24: Visual Instruction-tuned Adaptation for Radiology Report Generation](https://aclanthology.org/2024.bionlp-1.54/)
 - Code Repository: Open-source implementation and pre-trained models (GitHub: [Gla-AI4BioMed at RRG24](https://github.com/X-iZhang/RRG-BioNLP-ACL2024))
-  
+
+## How to Cite ✒️
+
+If you use this model in academic or research contexts, please cite:
+
+```bibtex
+@inproceedings{zhang-etal-2024-gla,
+    title = "Gla-{AI}4{B}io{M}ed at {RRG}24: Visual Instruction-tuned Adaptation for Radiology Report Generation",
+    author = "Zhang, Xi  and
+      Meng, Zaiqiao  and
+      Lever, Jake  and
+      Ho, Edmond S.L.",
+    editor = "Demner-Fushman, Dina  and
+      Ananiadou, Sophia  and
+      Miwa, Makoto  and
+      Roberts, Kirk  and
+      Tsujii, Junichi",
+    booktitle = "Proceedings of the 23rd Workshop on Biomedical Natural Language Processing",
+    month = aug,
+    year = "2024",
+    address = "Bangkok, Thailand",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2024.bionlp-1.54/",
+    doi = "10.18653/v1/2024.bionlp-1.54",
+    pages = "624--634",
+    abstract = "This paper introduces a radiology-focused visual language model designed to generate radiology reports from chest X-rays. Building on previous findings that large language models can acquire multimodal capabilities when aligned with pretrained vision encoders, we demonstrate similar potential with chest X-ray images. The model combines an image encoder (CLIP) with a fine-tuned large language model (LLM) based on the Vicuna-7B architecture. The training process involves a two-stage approach: initial alignment of chest X-ray features with the LLM, followed by fine-tuning for radiology report generation. The study highlights the importance of generating both FINDINGS and IMPRESSIONS sections in radiology reports and evaluates the model`s performance using various metrics, achieving notable accuracy in generating high-quality medical reports. The research also addresses the need for domain-specific fine-tuning to capture the intricate details necessary for accurate medical interpretations and reports."
+}
+```