M - QALM : A Benchmark to Assess Clinical Reading Comprehension and Knowledge Recall in Large Language Models via Question Answering (2024)

Abstract: There is vivid research on adapting Large Language Models (LLMs) to perform a variety of tasks in high-stakes domains such as healthcare. Despite their popularity, there is a lack of understanding of the extent and contributing factors that allow LLMs to recall relevant knowledge and combine it with presented information in the clinical and biomedical domain: a fundamental pre-requisite for success on down-stream tasks.Addressing this gap, we use Multiple Choice and Abstractive Question Answering to conduct a large-scale empirical study on 22 datasets in three generalist and three specialist biomedical sub-domains. Our multifaceted analysis of the performance of 15 LLMs, further broken down by sub-domain, source of knowledge and model architecture, uncovers success factors such as instruction tuning that lead to improved recall and comprehension. We further show that while recently proposed domain-adapted models may lack adequate knowledge, directly fine-tuning on our collected medical knowledge datasets shows encouraging results, even generalising to unseen specialist sub-domains. We complement the quantitative results with a skill-oriented manual error analysis, which reveals a significant gap between the models’ capabilities to simply recall necessary knowledge and to integrate it with the presented context.To foster research and collaboration in this field we share M-QALM, our resources, standardised methodology, and evaluation results, with the research community to facilitate further advancements in clinical knowledge representation learning within language models.

## introduction recent success in the application of proprietary large language models in the knowledge-intensive medical domain @xcite has sparked vivid research interest in applying smaller, figure 1 : the landscape of llm evaluation in the medical domain with representative evaluation tasks, organised by bloom's taxonomy of learning objectives (bold) @xcite . the success of such adaptation is typically established by measuring the performance on downstream tasks, by means of token overlap or semantic similarity-based metrics @xcite . to address their inherent weaknesses @xcite , research attempts to incorporate specific dimensions, such as factuality or faithfulness @xcite . two important problems remain, however. firstly, natural language generation (nlg) evaluation metrics are merely approximations of the phenomena they aim to measure, and their effectiveness is typically established by the degree of correlation to human judgements of the evaluated criteria given such difficulties, we approach the problem of evaluating llm adaptation from a complementary angle. specifically, we ask: do llms possess the necessary pre-requisites to succeed in the clinical and medical domains? without an established theory of how knowledge is acquired and organised in llms, the present work is guided by the established theories of knowledge acquisition in humans @xcite . typical nlg tasks, such as summarisation, are higher-level cognitives that require the understanding of learned knowledge and its application in new contexts @xcite . they build on the most fundamental capability of reading comprehension @xcite : the construction of a text-base and its integration with previously acquired background knowledge. in nlp research, this process is evaluated by openbook question answering (qa), the task of either generating (abstractive, aqa) or selecting among presented options (multiple-choice, mcqa) the correct answer for a question, where potentially not all necessary information is included in the question or the presented context. mcqa evaluation does not suffer from the issues pertaining to nlg metrics, as performance is established by exact match. thus, conclusions obtained from such evaluations tend to be more robust, if the quality of the benchmark is sufficient. therefore, in this paper we focus on the task of qa, to evaluate knowledge recall and comprehension pre-requisites of llms for successful adaptation to the medical domain. we present an exhaustive, publicly available qa benchmark called m-qalm including 16 mcqa datasets. to enable future research on nlg-based qa, we complement m-qalm by 6 high-quality aqa datasets, where the ground-truth answer is an unconstrained string. with such a standardized benchmark, we conduct an extensive evaluation of the capabilities of openly available general-purpose and medical llms, both "out-of-the-box" and after fine-tuning on m-qalm. our findings provide insights into the strengths and weaknesses of different llms across a range of datasets, question categories and qa tasks. overall, we find their performance lacking, both compared to humans and to proprietary llms. further analysis reveals promising tendencies of domain-specific pre-training and fine-tuning to bridge this gap and to generalise to new qa datasets. ## related work large open-domain qa benchmarks the availability of qa datasets from multiple domains and sources has enabled the curation of large and diverse qa benchmarks @xcite @xcite . such resource collections enable researchers to perform large-scale empirical studies to understand how well language models can generalise to new questions from new domains or sources, or how fine-tuning can impact this performance. while multiple studies exist in the general domain, to the best of our knowledge, no such large-scale study has been carried out for qa in the clinical domain. in this paper we aim to address this gap. evaluation in the clinical domain datasets that evaluate the lowest-level cognitive task of knowledge recall and reading comprehension in the medical domain have been proposed before @xcite @xcite . they feature questions commonly found in examinations like the us medical licensing exam (usmle). m-qalm unifies the existing literature by incorporating licensing exam questions from diverse regions, such as india and spain. we go beyond the scope of the general medical domain, covering specialist topics such as ophthalmology and alzheimer's disease. beyond factual recall and comprehension, @xcite collected a unified bio-medical benchmark, featuring nlp primitives such as sentence(-pair) classification or entity recognition and linking. aiming at higher, more task-specific cognitives, @xcite introduced mul-timedqa, including healthsearchqa, which requires models to generate high-quality free-form answers. similarly, @xcite introduced a multi-domain benchmark for evaluating generation and classification capabilities on a diverse set of in-hospital downstream tasks. other researchers looked to evaluate the quality and factuality of generation (umapathi et al., 2023) and synthesised general-purpose medical instructions @xcite . our work is complementary, because we evaluate knowledge recall and comprehension as a pre-requisite of higher-level cognitive tasks, such as understanding and application-the focus of previously discussed works. ## m-qalm datasets the primary goal of m-qalm is to develop a comprehensive, open-source repository of medical qa datasets to assess the recall of medical knowledge in llms. to obtain such a collection, we perform an exhaustive literature and resource search using the terms "clinical or medical", "question answering or qa" and include a dataset or resource if it satisfies the following criteria: (i) the language is english, as medical documents are usually written in english, even in non-english-speaking countries; (ii) the questions and answers are on general, specialist, or consumer-facing medical topics; (iii) the resource is openly available without restrictive licensing or data agreements; (iv) the resource evaluates the task of mcqa or aqa; (v) the ground truth is collected or reviewed by domain experts. the result is m-qalm-a comprehensive collection of 22 datasets designed to thoroughly evaluate the clinical knowledge of llms. ## empirical evaluation we investigate how well existing, open-source llms are able to recall clinical knowledge and integrate it into a given context in order to succeed on our benchmark. specifically, we focus on performance in the zero-shot setting, and after fine-tuning on m-qalm training portions. in the zero-shot setting: • rq1. how well do open-source llms recall necessary clinical knowledge when they are tested on m-qalm? • rq2. does open-domain instruction fine-tuning of llms improve their ability to do so? • rq3. does domain-specific fine-tuning improve performance on m-qalm? in the fine-tuned setting: • rq4. does finetuning on m-qalm improve performance on unseen data from datasets seen during training? • rq5. does fine-tuning improve performance on unseen m-qalm datasets? ## results and analysis in this section, we report and analyse the findings of our empirical study. ## error analysis in this section, we analyze the errors of llms on mcqa datasets. ## conclusions in this work, we introduce m-qalm, a comprehensive collection of clinical datasets comprising 16 multiple-choice and 6 abstractive questionanswering datasets. our study encompasses an extensive empirical investigation of open-source language models with up to 13 billion parameters. we assess their clinical and biomedical knowledge, their capacity to acquire such knowledge through training on m-qalm, and their ability to generalize to previously unseen datasets. our results highlight the strengths and limitations of llms on mcqa and aqa: while performing significantly better than a random guess baseline, they still fall significantly short in performance compared to proprietary language models and humans. this is true even after fine-tuning on m-qalm, which demonstrates potential improvements, especially in the context of instruction finetuned models like flan-t5. finally, we show inconsistencies arising from the use of different aqa metrics-in future work we will supplement the automated metrics by fine-grained expert-driven manual evaluation of llm's answers on m-qalm to learn to automate (some dimensions of) these expert judgments. based on our findings, we caution on the unconstrained use of open-source llms @xcite as assistants to help perform medical tasks or provide answers to medical queries, to experts or lay people alike, as they seem to lack the necessary medical domain knowledge. we make the dataset, experiment code and evaluation protocol publicly available foot_3 to allow future developers of medical llms to assess the foundations of their models' knowledge, as our evaluation shows that architecture of language models, the choice of datasets for pre-training and instruction fine-tuning can greatly impact their performance to the extent it can be assessed by m-qalm.