SinoXivLarge language models are increasingly used in clinical medicine, but their reliance on cloud servers conflicts with patient-privacy requirements and excludes resource-limited healthcare systems. Smal...
Purpose: To evaluate expert-rated informational quality and linguistic accessibility of responses generated by a contemporary large language model to common ophthalmic patient questions, and to explor...
As Large Language Models (LLMs) are increasingly deployed in autonomous, high-stakes environments, the fragility of current Reinforcement Learning from Human Feedback (RLHF) alignment protocols remain...
Large language models require high-quality human feedback for alignment and fine-tuning, yet platforms face the fundamental challenge of incentivizing valuable contributions while screening out potent...
Background Sepsis is a life-threatening organ dysfunction caused by a dysregulated host response to infection. Early and accurate prediction is crucial for reducing mortality. However, clinical settin...
In recent years, the application of Large Language Models (LLMs) in the legal field has gradually increased. As they evolved, their function has transitioned from simple legal knowledge extraction to ...
This paper introduces an empirical extension of the Deep Personal Privacy (DPP) framework, a novel paradigm that reconceptualizes privacy as resistance to inference rather than mere control over data ...
Background Artificial intelligence (AI), particularly large language models (LLMs), has emerged as a promising tool in healthcare, with potential applications in clinical decision support and dental e...
The large-scale deployment of Large Language Models (LLMs) is constrained by significant energy consumption and operational costs, with inference accounting for up to 90% of the total energy footprint...
Large language model (LLM)-based chatbots are increasingly integrated into various sectors of people's lives, including education, healthcare, and retail. As they become more ubiquitous, safety concer...
