Large language models (LLMs) are built on three core components: the neural network architecture, the training data, and the optimization process. The architecture, typically based on the transformer model, uses self-attention mechanisms to process sequences of text. Transformers consist of multiple layers, each containing attention heads and feed-forward networks. Attention heads allow the model to weigh the importance of different words in a sentence relative to each other. For example, in the sentence “The cat sat on the mat,” the word “cat” would receive more attention when predicting “sat” than “mat.” This architecture scales by increasing the number of layers (depth) and the size of hidden states (width), enabling the model to capture complex language patterns.
The second key component is the training data. LLMs are trained on vast, diverse text corpora, including books, articles, and websites. The data is preprocessed into tokens—subword units like “ing” or "tion"—using algorithms like Byte-Pair Encoding (BPE). Tokenization ensures rare words are represented efficiently, reducing vocabulary size while maintaining coverage. For instance, the word “unbelievable” might split into “un,” “believe,” and “able.” Data quality directly impacts performance: biased or low-quality data can lead to unreliable outputs. Developers often apply filters to remove harmful content or duplicate text, balancing breadth and cleanliness.
The third component is the training and optimization process. LLMs use unsupervised learning to predict the next token in a sequence, minimizing a loss function like cross-entropy. Training requires massive computational resources, often distributed across GPUs or TPUs, using frameworks like TensorFlow or PyTorch. Hyperparameters such as learning rate, batch size, and dropout are tuned to stabilize training. For example, a learning rate too high might cause the model to diverge, while too low slows progress. After pretraining, models are fine-tuned on task-specific data (e.g., question-answering pairs) using supervised learning. This step adapts the model’s general knowledge to specialized applications, ensuring it aligns with user needs.