Few shot chain-of-thought driven reasoning to prompt LLM s for open-ended medical question answering (2024)

Abstract: In this paper, we propose a modified version of the MedQA-USMLE dataset, named MEDQA-OPEN, which contains open-ended medical questions without options to mimic clinical scenarios, along with clinician-approved reasoned answers. Additionally, we implement a prompt driven by Chain of Thought (CoT) reasoning, CLINICR, to mirror the prospective process of incremental reasoning, reaching a correct response to medical questions. We empirically demonstrate how CLINICR outperforms the state-of-the-art 5-shot CoT-based prompt (Liévin et al., 2022). We also present an approach that mirrors real-life clinical practice by first exploring multiple differential diagnoses through MCQ-CLINICR and subsequently narrowing down to a final diagnosis using MCQ-ELIMINATIVE. Finally, emphasizing the importance of response verification in medical settings, we utilize a reward model mechanism, replacing the elimination process performed by MCQ-ELIMINATIVE.

## introduction large language models (llms) are increasingly utilized in the healthcare sector, particularly for patient query-related tasks. these llm-driven tools could potentially interpret and respond to patient inquiries, and provide information on symptoms, diseases, treatments, and healthcare guidelines @xcite . by analyzing vast amounts of medical literature and data, llms could also offer precise, up-to-date responses, improving patient education and engagement @xcite . the ability of llms to process natural language queries makes them accessible and userfriendly, thus enhancing the patient experience and satisfaction @xcite . as technology evolves, llms are expected to play a pivotal role in delivering personalized healthcare information, contributing to informed decision-making and better health outcomes @xcite . however, with the recent advances in prompt engineering techniques in the large language models space, there is an underlying requirement for accuracy and verifiability. ## background & related work the usmle-medqa dataset @xcite , a medical exam dataset, consists of questions sourced from professional medical board exams in the usa. the dataset is a comprehensive resource for investigating medical question answering in a standardized mcq examination format. the dataset offers complexity and challenge, however, the current version of the dataset is not practically useful to be deployed in a patient query-based healthcare system since four options are associated with each question thereby not being similar to in-clinic scenarios. recent advancements in the application of large language models (llms) for medical question answering have highlighted the potential of these models to demonstrate applicability in medical diagnostics, education, and research. @xcite explored augmenting black-box llms with medical textbooks to enhance clinical question an-swering. similarly, @xcite introduced medlm, exploring the efficacy of llms in handling diverse medical question-answering systems. furthermore, recent research highlights prompt engineering as a viable alternative to traditional fine-tuning approaches. @xcite demonstrated that prompt-based methods outperform fine-tuning when working with open-source medical language models, particularly in resourceconstrained environments. another noteworthy contribution is by @xcite , who proposed mededit, a framework for model editing using external knowledge bases to improve performance in medical qa. lastly, @xcite examined effective interaction methods between llms and medical professionals, questioning whether llms can correct physicians' responses, thus opening new avenues for improving collaboration in medical domains. the exploration of llms in generating not only accurate but also reasoning-based responses to medical questions, marks a significant step forward. models such as pubmedgpt @xcite and @xcite have established benchmarks on datasets like medqa through innovative approaches, including classification head, chain-of-thought, and knowledge grounding, highlighting the importance of not just what is answered, but how the answer is derived. these studies lay the groundwork for further advancements in medical qa using llms, particu-larly in developing more efficient and transparent interaction strategies to enhance clinical outcomes. answering medical questions involves the requirement of fact verification as well as the necessity of reasoning skills. these have been attempted to be addressed through the following methods: fact verification in llms: fact verification in llms involves computational linguistics, ai, and digital media. @xcite present detailed approaches to automated fact-checking, including claim detection, evidence retrieval, and verification. this process assesses truthfulness, identifies evidence, and generates explanations, enhancing robust verification systems @xcite . chain-of-thought prompting: initially, scaling language models up appeared to benefit more knowledge-intensive tasks than reasoning-heavy ones @xcite . nevertheless, @xcite demonstrated that llms could be applied to reasoning-intensive problems by prompting the model to generate step-by-step solutions, coined "chain-of-thought" (cot). cot prompting led to substantial improvements in many reasoningintensive tasks @xcite @xcite , allowing to bridge the gap with human-level performances. the work of @xcite evaluates llms using the mmlu dataset across diverse subjects like medicine, showcasing their broad applicability. additionally, @xcite emphasize the importance of model interpretability and explainability for trust in medical applications. ## approach in this section, we first define a task which can be used to evaluate the capability of large language models (llms) to be used as clinical support systems. we then describe prompt-based and verifierbased approaches to solve the task. ## conclusion and future work the incremental reasoning chain of thought prompting (clinicr) is a novel prompting methodology that follows the usual clinical approach of deciding on real-life clinical settings. we demonstrate that this strategy gives significantly better results than eliminative, which is primarily designed for mcq-type questions. further, we demonstrate that the verifier developed using reasoning performs much better at selecting agreeable responses from the llama-2 models. future research will explore the generalizability of this approach by testing on other open-source llms. this research focuses on improving the quality of responses generated using in-context learning and leveraging the models' parametric knowledge of medicine. we could further aim to integrate these methods with non-parametric knowledge sources, to boost the range of knowledge available, and improve the confidence within the output. for this, methods like retrieval augmented generation (rag), and knowledge graph integration could be explored.