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.

该论文介绍了我们在 MEDIQA-CORR 共享任务中的方法，该任务涉及医学专业人员策划的临床笔记中的错误检测和更正。我们的工作旨在评估在包含事实和不可靠信息的海量互联网数据上训练的大型语言模型（LLM）的能力。我们建议综合处理所有子任务，并建议采用独特的基于提示的上下文学习策略。我们将评估其在结合一般推理和医学知识的专门任务中的功效。在预测错误可能导致严重后果的医学系统中，我们建议利用自洽性和集成方法来提高错误更正和错误检测性能。