EM_Mixers at MEDIQA-CORR 2024: Knowledge-Enhanced Few-Shot In-Context Learning for Medical Error Detection and Correction

Abstract

This paper describes our submission to MEDIQA-CORR 2024 shared task for automatic identification and correction of medical errors in a given clinical text.We report results from two approaches: the first uses a few-shot in-context learning (ICL) with a Large Language Model (LLM) and the second approach extends the idea by using a knowledge-enhanced few-shot ICL approach.We used Azure OpenAI GPT-4 API as the LLM and Wikipedia as the external knowledge source.We report evaluation metrics (accuracy, ROUGE, BERTScore, BLEURT) across both approaches for validation and test datasets.Of the two approaches implemented, 1 our experimental results show that the knowledgeenhanced few-shot ICL approach with GPT-4 performed better with error flag (subtask A) and error sentence detection (subtask B) with accuracies of 68% and 64%, respectively on the test dataset.These results positioned us fourth in subtask A and second in subtask B, respectively in the shared task.

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