PromptMind Team at MEDIQA-CORR 2024: Improving Clinical Text Correction with Error Categorization and LLM Ensembles
Satya Kesav Gundabathula, Sriram R Kolar · 2024
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.