Large language models (LLMs) hold great promise in summarizing medical evidence. Most
recent studies focus on the application of proprietary LLMs. Using proprietary LLMs …

Prompt engineering, the process of arranging input or prompts given to a large language
model to guide it in producing desired outputs, is an emerging field of research that shapes …

Multilingual large language models (MLLMs) leverage advanced large language models to
process and respond to queries across multiple languages, achieving significant success in …

Two-step approaches, in which summary candidates are generated-then-reranked to return
a single summary, can improve ROUGE scores over the standard single-step approach. Yet …

Summarization models often generate text that is poorly calibrated to quality metrics
because they are trained to maximize the likelihood of a single reference (MLE). To address …

A major challenge of our time is reducing disparities in access to and effective use of digital
technologies, with recent discussions highlighting the role of AI in exacerbating the digital …

Objective Clinical notes contain unstructured representations of patient histories, including
the relationships between medical problems and prescription drugs. To investigate the …

According to the parallel architecture, syntactic and semantic information processing are two
separate streams that interact selectively during language comprehension. While …

Contrastive learning of sentence representations has achieved great improvements in
several natural language processing tasks. However, the supervised contrastive learning …

Purpose: Radiology report generation, translating radiological images into precise and
clinically relevant description, may face the data imbalance challenge–medical tokens …