Towards Trustworthy Summarization of Cardiovascular Articles: A Factuality-and-Uncertainty-Aware Biomedical LLM Approach (2025)

Abstract: While large, biomedical documents with complex terminology are in need of being understood more easily and efficiently, summarizing this kind of content can be problematic, as Large Language Models (LLMs) aren’t always trustworthy. Considering the importance of comprehending Cardiovascular Diseases, we study in depth the ability of different state-of-the-art biomedical LLMs to generate factual and certain summaries in this topic, and examine which generation choices can influence their trustworthiness. To that end, besides using factuality metrics, we employ techniques for token-level uncertainty estimation, an area that has received little attention from the scientific community. Our results reveal dissimilarities between LLMs and generation methods, and highlight connections between factuality and uncertainty metrics, thereby laying the groundwork for further investigation in the area.

## introduction biomedical researchers worldwide try to solve vital medical problems and publish scientific discoveries. due to the exponentially increasing amount of scientific publications, summarizing them is vital, as they enable medical practitioners to keep up with the literature in an efficient manner. for that reason, it is crucial that the summary is accurate, as a minor mistake in explaining a medical concept or an unclear medical advice to treat a disease can have severe consequences for the health of patients. large language models have recently been used to process and understand this kind of information in depth. in recent years, llms have gained much attention from the scientific community, as they have been especially transformative for generative tasks, such as text summarization, machine translation, and question answering @xcite ). text summarization, the task of our interest, is the process of creating a brief, accurate, and coherent summary of a longer text document. llms have greatly facilitated this task by providing the option to generate new text with the most salient information (i.e., abstractive summarization; @xcite ). in biomedicine, scientific findings tend to be reported in large documents with complex terminology, so summarizing scientific content can make important, clinical information accessible to researchers and clinicians more easily and efficiently @xcite . however, llms may misrepresent their confidence and have specifically been shown to overestimate their knowledge and certainty level (they don't know what they don't know). as a result, they may confidently generate summaries with hallucinations or ambiguities @xcite that can lead to misinformation with potentially severe consequences in medical contexts. although previous studies have explored uncertainty in biomedicine @xcite , most either attempt to detect confidence expressions in text @xcite , or focus on sequence-level uncertainty @xcite @xcite , frequently requiring the use of external models, repetitive sampling, or dedicated loss functions. instead, we focus on simple, flexible, token-level uncertainty metrics, which can detect fine-grained local uncertainties, while also avoiding sequence-level limitations, such as length bias and over-correction that arise when collapsing token distributions into a single score. this area has received little attention in biomedical summarization, despite its importance in early detection and avoidance of misleading outputs. we thus attempt to address this gap and present early findings, assessing several biomedical llms on summarization of literature related to cardiovascular diseases. we argue that a trustworthy model should not only achieve high factuality but also high confidence, especially for factually correct generations. we thus examine different factors that could influence factuality and uncertainty, such as decoding strategies and fine-tuning. we find that the proposed factuality and confidence metrics don't correlate, but motivate further token-level analysis and more dedicated uncertainty metrics. evaluation of factuality in biomedical text generation is an ongoing challenge. @xcite introduced alignscore, a holistic metric, based on a general function of information alignment of text and its unified framework, which achieved substantial improvements over previous metrics. @xcite advocated a new evaluation metric that computes factual accuracy from pieces of generated text and was used to compare the performance of different llms. additionally, @xcite proposed a new evaluation framework, longdocfactscore, for detecting human factuality targeting specifically summarized, long documents. finally, @xcite introduced a human-annotated dataset of llm-generated summaries of clinical texts (treatfact) and revealed significant performance gaps in terms of factuality for open-source llms. previous work has comprehensively examined uncertainty in natural language generation (nlg) systems @xcite and has explored strategies to address uncertainty with the goal of making llms more trustworthy, especially in biomedicine @xcite . @xcite studied summarization decoders in both blackbox and whitebox ways by focusing on the entropy of the models' predictions and revealed that features, such as the sentence position and the syntactic distance between adjacent pairs of tokens, influence uncertainty. @xcite focused on token-level uncertainty and proposed a method for non-exchangeable conformal prediction, which was shown to improve text generation quality. finally, @xcite introduced a token-level uncertainty method named claim conditioned probability (ccp), disentangling claim-specific uncertainty from model decisions on surface forms, etc. ## methodology we propose two different metrics of uncertainty, we test them to commonly used decoding methods, and we measure their correlation to factual accuracy. ## experimental setup below we describe the different features that are set up for the conduction of the experiments. ## results in this section we present the comparisons across the metrics and models described above, accounting for different aspects, like the overall performance of the llms, the effect of fine-tuning on factuality and uncertainty, as well as differences between the decoding strategies. finally, we assess the correlation between the factuality and uncertainty metrics. ## conclusions & future work we evaluated the trustworthiness of state-of-the-art biomedical llms on summarization using both factuality and token-level uncertainty metrics. results showed that model choice and decoding strategy influenced trustworthiness, even though we applied standard values on the sampling strategies for k and p, with qwen variants performing best and sampling-based methods, especially top-p, producing more factual and confident summaries. several promising directions for future work include expanding the evaluation to larger and more diverse biomedical datasets to improve statistical reliability and test the generalizability of tokenlevel uncertainty metrics across domains. another direction is investigating different decoding hyperparameters to gain insights into how generation settings affect factuality and uncertainty. finally, evaluating larger biomedical llms, including closedsource models, and incorporating human evalua