Large Language Models to understand biomedical text
Yuan Luo, Director, Institute for Artificial Intelligence in Medicine - Center for Collaborative AI in Healthcare; Associate Professor of Preventive Medicine (Health and Biomedical Informatics), McCormick School of Engineering and Pediatrics
Abstract: : Large Language Models such as transformer-based models have been wildly successful in setting state-of-the-art benchmarks on a broad range of natural language processing (NLP) tasks, including question answering (QA), document classification, machine translation, text summarization, and others. Recently, the release of OpenAI’s free tool ChatGPT demonstrated the ability of large language models to generate content, with anticipations on its possible uses and potential controversies. The ethical and acceptable boundaries of ChatGPT’s use in scientific writing remain unclear. I will talk about our research on exploring large language models, e.g., long-sequence transformers and GPT style models, in the clinical and biomedical domains. Our work examines the adaptability of these large language models to a series of clinical NLP tasks including clinical inferencing, biomedical named entity recognition, EHR based question answering, interoperability etc.
Cost: free