This groundbreaking study explores the expanse of Large Language Models (LLMs), such
as Generative Pre-Trained Transformer (GPT) and Bidirectional Encoder Representations …

User modeling (UM) aims to discover patterns or learn representations from user data about
the characteristics of a specific user, such as profile, preference, and personality. The user …

Electronic Health Records (EHRs) and routine documentation practices play a vital role in
patients' daily care, providing a holistic record of health, diagnoses, and treatment. However …

While large language models (LLMs) have showcased their potential in diverse language
tasks, their application in the healthcare arena needs to ensure the minimization of …

Perioperative anesthetic management entails a multitude of decision-making processes
within complex medical scenarios. These demand the continuous and dynamic execution of …

The global prevalence of mental health disorders is increasing, leading to a significant
economic burden estimated in trillions of dollars. In automated mental health diagnosis, the …

Recently, artificial intelligence generated content (AIGC) has been receiving increased
attention and is growing exponentially. AIGC is generated based on the intentional …

Black-box few-shot text classification handles text classification in limited data without
accessing the parameters and gradients of language models (LMs). Existing black-box …

Medical abstractive summarization faces the challenge of balancing faithfulness and
informativeness. Current methods often sacrifice key information for faithfulness or introduce …

Large Language Models (LLMs) are increasingly adopted for applications in healthcare,
reaching the performance of domain experts on tasks such as question answering and …