  This paper describes our submission to the MEDIQA-Chat 2023 shared task for
automatic clinical note generation from doctor-patient conversations. We report
results for two approaches: the first fine-tunes a pre-trained language model
(PLM) on the shared task data, and the second uses few-shot in-context learning
(ICL) with a large language model (LLM). Both achieve high performance as
measured by automatic metrics (e.g. ROUGE, BERTScore) and ranked second and
first, respectively, of all submissions to the shared task. Expert human
scrutiny indicates that notes generated via the ICL-based approach with GPT-4
are preferred about as often as human-written notes, making it a promising path
toward automated note generation from doctor-patient conversations.
