## 2 Overview of Large Language Models and Their Taxonomy
DeepMind's Sparrow [22] provides reliable and safe responses. This solution can be helpful if a model initially provides unethical or misleading responses, as human feedback can guide learning to correct them, such as responding to sensitive questions or preventing bias. _Instruction Tuning_ aims to make the model responsive to prompts with clear instructions, with the goal of improving the model's ability to follow specific instructions using a set of human-generated examples. This approach makes the model more reactive. For example, Google trained the T5 model with instruction tuning (FLAN-T5), allowing it to better respond to prompts with specific instructions. _Mixture of Experts_ (MoE) divides a model into subnetworks (\"experts\"), each specialized in different tasks. During inference, only some experts are activated, reducing computational load. Examples of this method include Google's Generalist Language Model (GlaM) [15], Switch Transformer, and Mistral. For example, GLaM uses MoE to activate only a part of the model during inference, reducing computational costs. Despite the various architectures and training techniques used by different language models, one common issue across all of them is hallucinations. This phenomenon occurs when a model generates responses that appear plausible but are actually incorrect or entirely fabricated. Hallucinations arise because language models, whether autoregressive like GPT, encoder-decoder like T5, or models like BERT, rely on statistical correlations between data rather than true semantic understanding. While these models are capable of learning complex linguistic structures, their lack of deep content comprehension often leads to the generation of inaccurate or non-existent information, especially when dealing with rare data or data that were not adequately represented during training. This issue is a common challenge for all large-scale language models and represents one of the main areas of research aimed at improving their reliability and ability to generate correct and verified responses. _LLMs_ are finding promising applications in the medical field in various areas:  
* _Diagnostic support: LLMs_ can analyze clinical data and assist in the diagnostic process by suggesting possible diagnoses or treatment plans based on symptom descriptions;
* _Natural language interpretation_: They can help translate clinical notes or medical reports into structured information useful for analysis;
* _Medical question answering: LLMs_ can answer complex questions related to medications, treatments, or clinical conditions;
* _Assistance in research_: They can help researchers find relevant information in large volumes of scientific articles or generate summaries of studies. GPT, Mistral, and BERT are widely used in medical and general natural language processing applications. GPT has often been employed for medical chatbots, clinical decision support, and scientific text generation. It can summarize documents, answer medical questions, and assist in medical training through simulations. Mistral, with its focus on efficiency and high performance, is valuable for applications in medical text analysis and the automation of healthcare documentation processes. BERT is frequently used for text classification, information extraction, and natural language understanding in clinical data. These models, along with their specialized variants (see Table 1), play a crucial role in advancing artificial intelligence in medicine, enhancing diagnostic accuracy, clinical support, and access to medical knowledge.