2.0 Basics of LLMs

Large Language Models (LLMs) are built on sophisticated mechanisms that drive their advanced language understanding and generation capabilities. In particular, the transformer architecture has significantly enhanced the performance of LLMs. This chapter explains the technical elements that are core to LLMs.

2.1 Explanation of the Transformer Model

The transformer model is the foundational architecture of LLMs. Unlike traditional neural networks (such as RNNs or LSTMs) that have limitations in processing sequential data, transformers can process data in parallel and handle long-range dependencies effectively. This feature enables LLMs to handle large text datasets quickly and accurately.

2.2 Attention Mechanism

The most distinctive feature of the transformer model is the Attention Mechanism. This mechanism explicitly models the dependencies between words in the context, allowing for a deeper understanding of relationships between words. In particular, the Self-Attention Mechanism calculates the level of attention each word in a sentence should pay to every other word, helping to grasp the overall context. This mechanism is one reason LLMs can generate highly natural text.

2.3 Key Models: BERT, GPT, T5

Several prominent LLMs address natural language processing challenges using different approaches. For instance, BERT (Bidirectional Encoder Representations from Transformers) excels at understanding context in both directions, capturing relationships before and after a word. GPT (Generative Pre-trained Transformer) is primarily specialized in text generation, with strong abilities to continue text based on an initial prompt. T5 (Text-to-Text Transfer Transformer) treats all NLP tasks as text transformation tasks, making it a highly flexible model.

These models are fine-tuned for specific tasks, allowing them to be applied to a wide range of NLP tasks, including machine translation, question answering, and summarization. Choosing the right model is a critical step for engineers working on real-world projects.

In summary, the basic structure of LLMs relies on transformer models and attention mechanisms, which are key to their performance. Each model has unique features, and selecting the optimal model is essential for project success.