LLMs: an overview
In the rapidly evolving field of artificial intelligence, LLMs have significantly advanced natural language processing (NLP) and understanding. These models, characterized by their extensive number of parameters and trained on large datasets, have demonstrated remarkable capabilities across a wide set of language-related tasks.
The journey of language models began with statistical approaches that relied on probabilistic methods to predict word sequences. These early models, while creating the foundations, were limited by their reliance on fixed-size context windows and the inability to capture long-range dependencies. However, as we also discussed in Chapter 4, Unsupervised Graph Learning, with the advent of neural networks, the field has undergone a significant shift, introducing models capable of learning word embeddings. In order to improve the ability to capture long-range dependencies, the initial neural network models were based on the Long-Short Term Memory...
