## 2 Overview of Large Language Models and Their Taxonomy
Colors represent different _LLM_ architectures. The most recent models, such as GPT-4 [14], are trained on much larger volumes of data compared to their predecessors, reflecting the increasing availability and use of data to enhance model capabilities. The growth in the number of parameters is evident over time, with models like GPT-4 and GLaM [15] having orders of magnitude more parameters than earlier models such as GPT-1 or BERT [16]. One of the most significant features of _LLMs_ is their ability to address various aspects of natural language processing. Below is a list highlighting the key capabilities they cover:  
* _Text completion:_ Generating sentences or paragraphs from a given input;
* _Translation:_ Translating text from one language to another;
* _Question answering:_ Answering questions based on context or prior knowledge;
* _Summarization:_ Summarizing long texts;
* _Content creation:_ Writing articles, stories, essays, or code;
* _Conversations:_ Simulating conversations with users, like chatbots. _LLMs_ represent one of the most significant advancements in the field of AI, due to their ability to handle and understand natural language with levels of accuracy and fluency never seen before. A list of some of the most well-known models follows:  
1. _GPT-3 [17]_ and _GPT-4_, by OpenAI, are among the most famous language models. GPT-3, for example, has 175 billion parameters and is capable of generating highly complex and coherent texts. 2. _BERT_ (Bidirectional Encoder Representations from Transformers) was designed by Google and is distinguished by its ability to consider the context both before and after a word, making it highly effective for tasks such as understanding meaning in sentences. 3. _T5_ (Text-To-Text Transfer Transformer) [18] transforms all natural language processing (NLP) tasks into a text generation format, making it extremely versatile. 4. _PaLM_ (Pathways Language Model) [19]: Another model developed by Google with billions of parameters, aimed at competing with GPT in various linguistic tasks. 5. _Mistral_[20] is another notable language model that has gained attention for its innovative approach. Developed by a team of researchers focused on advancing AI, Mistral is designed to improve the efficiency and flexibility of natural language understanding. Unlike traditional models, Mistral uses a unique architecture that optimizes training efficiency while maintaining high performance across a wide range of NLP tasks. Mistral features billions of parameters, similar to models like GPT-3 and PaLM, but with a focus on achieving optimal performance using a more compact model. This allows Mistral to perform complex tasks with relatively less computational resource usage, making it an attractive choice for real-time applications and systems with limited computational power. In addition to text completion, summarization, and translation, Mistral has shown impressive capabilities in tasks such as semantic analysis, context-aware content generation, and complex question answering. It also supports domain-specific applications, allowing customization for specialized fields like healthcare or legal sectors. By combining an advanced transformer architecture with a focus on computational efficiency, Mistral has quickly emerged as a strong competitor in the field of large language models, delivering results comparable to other well-established models while offering potential advantages in terms of performance scalability and energy efficiency. _LLMs_ can be classified based on different models used for training, with some of the most popular being as follows. _Reinforcement Learning from Human Feedback_ (RLHF) [21] uses human feedback to improve the responses of an _LLM_, aligning the model with desired behaviors. Learningoccurs through reinforcement algorithms such as Proximal Policy Optimization (PPO). This technique has been used in many cases; for example, OpenAI uses RLHF in advanced versions of ChatGPT to reduce inappropriate responses or align the model with ethical values.