The foundation of any large language model lies in its three key elements: the dataset, the model architecture, and the computational resources.
Dataset
The quality and size of the dataset are crucial. LLMs learn from the data they are trained on, so the breadth and diversity of this data determine the model’s ability to understand and generate human-like text. This dataset typically includes vast amounts of text from the web, literature, scientific articles, and more, encompassing a wide array of knowledge and language use.
Model Architecture
Most LLMs are based on the Transformer architecture, allowing for more effective handling of sequential data than prior models like RNNs or LSTMs. The Transformer uses mechanisms like self-attention to weigh the importance of different words within a sentence or passage, regardless of their position. This architecture supports the model in capturing deeper linguistic structures and meanings, which is essential for tasks requiring a nuanced understanding of language.
Computational Resources
Training LLMs require significant computational power. High-performance GPUs, TPUs, or clusters of these are typically employed to manage the enormous computational load. The cost of these resources can be substantial, as training a state-of-the-art model like GPT-3 or Turing-NLG involves weeks of continuous computation, consuming a lot of electrical power and generating considerable heat, which also needs to be managed.