  This paper describes our approach to the MEDIQA-CORR shared task, which
involves error detection and correction in clinical notes curated by medical
professionals. This task involves handling three subtasks: detecting the
presence of errors, identifying the specific sentence containing the error, and
correcting it. Through our work, we aim to assess the capabilities of Large
Language Models (LLMs) trained on a vast corpora of internet data that contain
both factual and unreliable information. We propose to comprehensively address
all subtasks together, and suggest employing a unique prompt-based in-context
learning strategy. We will evaluate its efficacy in this specialized task
demanding a combination of general reasoning and medical knowledge. In medical
systems where prediction errors can have grave consequences, we propose
leveraging self-consistency and ensemble methods to enhance error correction
and error detection performance.
