Browsing: model

The most common objection to running a small language model (SLM) is that it does not know enough, an objection that isn’t well calibrated. In reality, no model on the market is a dependable store of facts, and general capability benchmark scores turn out not to predict knowledge reliability at all. The two measures are…

  Hallucinations are one of the best-known problems that large language models (LLMs) may experience when generating responses. They occur when a model produces a response that is factually incorrect, nonsensical, or simply made up, typically due to the model’s lack of internal knowledge on the matter.

Large language models (LLMs) show considerable potential for atrial fibrillation (AF) management, yet current clinical applications frequently remain suboptimal due to accuracy limitations. To address these limitations, this study developed PULSE (Potentiated User-friendly LLM-driven Search Engine), a novel knowledge-enhanced, domain-aware LLM agent specifically designed to improve AF patient self-management across the entire care continuum. The…