Large Language Models (LLMs) and Natural Language Processing (NLP)

General concepts undelying LLMs

Large Language Models (LLMs) are artificial intelligence technologies revolutionizing how we interact with language. They are based on Natural Language Processing (NLP), a field studying the interaction between computers and human language. LLMs are NLP tools that enable tasks like translation and text generation. Initially based on statistical approaches, they underwent a significant shift with the advent of artificial neural networks (NNs), models mimicking the human brain's functioning and capable of handling language complexity.

The key concepts underlying LLMs' functioning include:

• Deep Learning: LLMs use deep neural networks, featuring many interconnected layers of neurons, to analyze and learn from textual data.

• Self-Supervised Learning: LLMs learn from the data itself, eliminating the need for explicit labels or instructions.

• Transformer-Based Architecture: Transformers are neural network models particularly suited for natural language processing. They efficiently analyze a sentence's context, overcoming previous models' limitations, using a mechanism called "attention."

• Word Embedding: To be processed by LLMs, texts are transformed into numerical representations called word embeddings.