@inproceedings{oh-etal-2024-use,
    title = "How to use Language Models for Synthetic Text Generation in Cerebrovascular Disease-specific Medical Reports",
    author = "Oh, Byoung-Doo  and
      Kim, Gi-Youn  and
      Kim, Chulho  and
      Kim, Yu-Seop",
    editor = "Deshpande, Ameet  and
      Hwang, EunJeong  and
      Murahari, Vishvak  and
      Park, Joon Sung  and
      Yang, Diyi  and
      Sabharwal, Ashish  and
      Narasimhan, Karthik  and
      Kalyan, Ashwin",
    booktitle = "Proceedings of the 1st Workshop on Personalization of Generative AI Systems (PERSONALIZE 2024)",
    month = mar,
    year = "2024",
    address = "St. Julians, Malta",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.personalize-1.2",
    pages = "10--17",
    abstract = "The quantity and quality of data have a significant impact on the performance of artificial intelligence (AI). However, in the biomedical domain, data often contains sensitive information such as personal details, making it challenging to secure enough data for medical AI. Consequently, there is a growing interest in synthetic data generation for medical AI. However, research has primarily focused on medical images, with little given to text-based data such as medical records. Therefore, this study explores the application of language models (LMs) for synthetic text generation in low-resource domains like medical records. It compares the results of synthetic text generation based on different LMs. To achieve this, we focused on two criteria for LM-based synthetic text generation of medical records using two keywords entered by the user: 1) the impact of the LM{'}s knowledge, 2) the impact of the LM{'}s size. Additionally, we objectively evaluated the generated synthetic text, including representative metrics such as BLUE and ROUGE, along with clinician{'}s evaluations.",
}