M ed E x QA : Medical Question Answering Benchmark with Multiple Explanations (2024)

Abstract: This paper introduces MedExQA, a novel benchmark in medical question-answering, to evaluate large language models’ (LLMs) understanding of medical knowledge through explanations. By constructing datasets across five distinct medical specialties that are underrepresented in current datasets and further incorporating multiple explanations for each question-answer pair, we address a major gap in current medical QA benchmarks which is the absence of comprehensive assessments of LLMs’ ability to generate nuanced medical explanations. Our work highlights the importance of explainability in medical LLMs, proposes an effective methodology for evaluating models beyond classification accuracy, and sheds light on one specific domain, speech language pathology, where current LLMs including GPT4 lack good understanding. Our results show generation evaluation with multiple explanations aligns better with human assessment, highlighting an opportunity for a more robust automated comprehension assessment for LLMs. To diversify open-source medical LLMs (currently mostly based on Llama2), this work also proposes a new medical model, MedPhi-2, based on Phi-2 (2.7B). The model outperformed medical LLMs based on Llama2-70B in generating explanations, showing its effectiveness in the resource-constrained medical domain. We will share our benchmark datasets and the trained model.

## introduction recent advancements in large language models (llms) have not only enhanced their understanding of medical domain text but also improved their ability to generate coherent text with correct medical knowledge @xcite . chatbots, powered by these llms, have emerged as indispensable tools, offering unprecedented opportunities to enhance patient care, streamline clinical decision-making processes, and medical knowl-edge retrieval @xcite @xcite . moreover, open-source medical llms further enhance the usability of such technologies in hospitals by resolving the privacy concerns associated with patient data @xcite @xcite . this research in medical llms has been facilitated by the introduction of question-answering (qa) datasets that serve as benchmarks for evaluating the model's understanding of medical domain knowledge @xcite @xcite . the benchmark qa datasets typically consist of multiplechoice questions (mcq), enabling researchers to readily assess the capabilities of llms in comprehending and responding to diverse medical inquiries. thus, the diversity within these datasets is a key component in creating a rigorous assessment benchmark for complex medical concepts. nonetheless, certain areas within the medical domain, such as speech language pathology, still remain uncovered by the current benchmark datasets. as current medical qa benchmarks are often structured as mcq, classification accuracy is used as an evaluation metric. however, classification accuracy alone may not adequately assess whether llms possess the nuanced medical expertise required for reasoned responses. the explanation and rationale behind the selection of a particular choice by an llm would provide a deeper understanding of the model's capabilities and limitations in generating responses to intricate medical questions. this comprehensive evaluation, delving into the explanation and rationale, is especially important in clinical settings where misleading information such as hallucinations produced by llms can have serious consequences. in order to assess the quality of the model explainability, the dataset should include a golden explanation for the reasoning behind the answer. additionally, since there are often multiple ways to express the same rationale in text, an ideal dataset would provide a multiple set of explanations for a single qa pair. however, current benchmark datasets are not focused on providing explanations as they often lack explanations entirely or only a subset of the dataset comes with an explanation @xcite @xcite . this limitation highlights the need for improved datasets that are explicitly designed to include comprehensive explanations. to address this issue, this paper presents a novel qa benchmark, medexqa, with two sets of explanations, aiming to provide a more comprehensive evaluation of llms in the medical domain. to diversify the knowledge coverage in the current datasets, our proposed benchmark consists of five underrepresented specialties in current datasets: biomedical engineering, clinical laboratory science, clinical psychology, occupational therapy, and speech language pathology. in this work, the datasets were used to benchmark the performance of an extensive list of llms, including those trained with medical domain text. with this comprehensive benchmark evaluation, we explored the effects of medical domain-specific training. additionally, to diversify the pool of open-source medical llms which are currently almost all based on the llama2 model, we introduce our own trained model, medphi-2, a phi-2 model trained with medical domain text. our medphi-2 model outperformed medical llms based on the llama2-70b model in generating explanations for the rationale behind the answer. the contributions of this paper are as follows: the datasets include two explanations for each question and answer pairs. while mmlu provides a comprehensive set of questions, it lacks explanations for the answers, thereby limiting the dataset's evaluation to mere multiple-choice classification accuracy. ## medexqa datasets we introduce medexqa, a novel qa benchmark designed to tackle the limitations of existing benchmarks by incorporating two sets of explanations. this approach aims to offer a more thorough evaluation of performance in five underrepresented specialties in the medical domain: biomedical engineering, clinical laboratory science, clinical psychology, occupational therapy, and speech language pathology. ## methods for all the experiments in this paper, both training and evaluation, we used 8 a6000 gpus. ## conclusion our medexqa benchmark proposes an effective methodology for evaluating llms beyond classification accuracy which can be used to assess the explainability of medical llms. while, the findings reveal that the generation of coherent and accurate explanations remains a challenging frontier for the current medical llms, the results also highlight an opportunity for a more robust automated comprehension assessment for llms because generation evaluation with multiple explanations aligned better with human assessment. we also find that the 'speech language pathology' dataset posed challenges for all language models, including gpt4. speech language pathology could potentially be attributed to several factors, with one prominent explanation being the absence of relevant text in the corpora used to train the foundation model. as speech language pathology is a highly specialized field that encompasses a wide range of topics related to rare diseases or disorders of speech and language, the collection of high-quality text for this specialty can be very challenging. however, it is important to acknowledge that confirming this hypothesis definitively poses a challenge due to the proprietary nature of the pretraining corpora used for training llms. through the development and evaluation of our medphi-2 model, we underscore the importance of targeted pretraining and fine-tuning strategies in improving explanation quality. the model showed the significant potential of llms in enhancing medical qa with explanations. our benchmark and model will set the foundation for future advancements in medical research by facilitating the development and evaluation of medical llms.