KU - DMIS at MEDIQA - CORR 2024: Exploring the Reasoning Capabilities of Small Language Models in Medical Error Correction (2024)

Abstract: Recent advancements in large language models (LM) like OpenAI’s GPT-4 have shown promise in healthcare, particularly in medical question answering and clinical applications. However, their deployment raises privacy concerns and their size limits use in resource-constrained environments.Smaller open-source LMs have emerged as alternatives, but their reliability in medicine remains underexplored.This study evaluates small LMs in the medical field using the MEDIQA-CORR 2024 task, which assesses the ability of models to identify and correct errors in clinical notes. Initially, zero-shot inference and simple fine-tuning of small models resulted in poor performance. When fine-tuning with chain-of-thought (CoT) reasoning using synthetic data generated by GPT-4, their performance significantly improved. Meerkat-7B, a small LM trained with medical CoT reasoning, demonstrated notable performance gains. Our model outperforms other small non-commercial LMs and some larger models, achieving a 73.36 aggregate score on MEDIQA-CORR 2024.

## introduction large language models (lm) have recently made significant advancements, finding usefulness across diverse applications in healthcare and medicine @xcite . for instance, openai's gpt-3.5 @xcite and gpt-4 @xcite have demonstrated their capabilities by achieving remarkable accuracy on standardized tests like the united states medical licensing examination (usmle). they have also shown excellence in real-world clinical applications-from responding to queries to diagnosing complex cases @xcite @xcite . however, deploying proprietary lms in this sensitive sector presents significant challenges, primarily due to privacy concerns and the need for secure @xcite with generated dataset to enhance its error detection and correction capabilities. for these reasons, smaller open-sourced lms are emerging as alternatives. for instance, models such as mistral @xcite and biomistral @xcite come with manageable sizes that are more suitable for deployment on local servers, while mitigating the security issues. how-ever, because these models have significantly fewer parameters (typically 7b) compared to large lms (more than 100b), there are doubts about whether these models can provide factual responses based on their parametric knowledge. this necessitates rigorous verification before being deployed especially in the medical domain, where reliability is of utmost importance. in this paper, we evaluate the reliability of small lms in the medical domain. for this purpose, we utilize the mediqa-corr 2024 shared task @xcite , which tasks models with identifying potential errors in clinical notes and correcting them. this task assesses the ability of models to address common medical sense errors, enabling us to verify their reliability and identify hallucinations in small language models. our initial experiment found that when small lms were evaluated in a zero-shot setting or trained using training data through simple supervised finetuning, their performance fell short of expectations. notably, the scores were similar to random guessing in a binary classification task. this result suggests solving complex medical problems is challenging for small models lacking advanced reasoning capabilities. thus, we hypothesized that fine-tuning the model with chain-of-thought (cot) reasoning @xcite could effectively equip the model with these necessary reasoning capabilities. to implement this, we generated reasoning paths between the inputs and outputs of the training dataset using gpt-4 and then trained the model not only to generate correct answers but also to provide the underlying reasoning for each decision (figure 1 ). this approach resulted in noticeable performance improvements, confirming the critical role of cot reasoning in solving complex medical problems. furthermore, we observed that small lms could benefit from reasoning capabilities aquired from other tasks. specifically, meerkat-7b @xcite , trained on an extensive medical cot reasoning dataset for usmle-style questions, showed greater performance improvements compared to other small lms. this significant improvement highlights the importance of reasoning capabilities for small lms to generate reliable responses. using this approach, we achieved an aggregate score of 73.36 for the natural language generation (nlg) evaluation, 63.46 for binary classification accuracy in detecting the presence of an error (error flag accuracy), and 61.51 for accuracy in identifying the specific sentence containing the error (error sentence accuracy) on the test set. despite its much smaller size relative to proprietary large lms, meerkat-7b demonstrated competitive performance in the mediqa-corr 2024 shared task, achieving the best score among noncommercial/small lms. this achievement is particularly significant considering the dominance of gpt-4-based frameworks among other teams. @xcite involves identifying medical errors in clinical notes and correcting them. this task is broken down into the following three sub-tasks: (1) binary classification, determining whether the clinical text contains a medical error or not, (2) span identification, detecting the specific text span associated with the error, and (3) natural language generation (nlg), creating a corrected version of the text in a freeform format. sub-tasks 2 and 3 are performed only when an error exists in the given note. ## experimental settings in all our experiments, we utilized eight 80gb nvidia a100 gpus. when fine-tuning, we used a learning rate of 1e-6 and a batch size of 128. 2 for generating the cot dataset, we used gpt-4 turbo (gpt-4-1106-preview) through the openai api. ## conclusion in this study, we explored the capabilities of small open-sourced language models in medical error correction and the effect of cot reasoning on this problem. our findings confirm that cot reasoning capabilities are highly encouraged for the task of clinical note correction, especially for small lms. particularly, meerkat-7b, initially trained to solve complex medical questions using an exaccurate and trustworthy medical assistants.