At the heart of every modern Large Language Model (LLM) lies the Transformer architecture, a revolutionary design introduced in the 2017 paper “Attention Is All You Need.” This architecture discarded the sequential processing of older recurrent neural networks (RNNs) in favor of a parallelizable, attention-driven mechanism. The core innovation is the self-attention mechanism, which allows the model to weigh the importance of every word in a sentence relative to every other word, regardless of distance. This enables it to capture long-range dependencies and contextual nuances—like understanding that “it” in a paragraph refers to a “cat” mentioned several sentences prior.
A Transformer consists of two main stacks: an encoder and a decoder. In models like GPT (Generative Pre-trained Transformer), which are decoder-only, the architecture is simplified. The model processes input tokens (words or sub-words converted into numerical vectors) simultaneously. Each token flows through multiple layers, each containing two key sub-components: a Multi-Head Self-Attention layer and a Feed-Forward Neural Network.
In the attention layer, the model creates for each token a Query, Key, and Value vector. The Query of one token is compared against the Keys of all other tokens to generate a set of attention scores—a probability distribution highlighting which tokens are most relevant. These scores then weight the Values, producing a refined output for each token that now contains contextual information from the entire sequence. “Multi-Head” attention performs this process in parallel across multiple “heads,” each learning to focus on different types of relationships (e.g., syntactic vs. semantic).
The output from attention is passed through a position-wise feed-forward network (a small, separate neural network for each token) for further processing. Critically, residual connections and layer normalization are used after each sub-layer to stabilize training across these deep networks. Since the model processes tokens in parallel, it has no inherent sense of order; this is injected via positional encodings—unique sinusoidal or learned vectors added to each token’s embedding to denote its position in the sequence.
LLMs are not trained from scratch for a single task. They undergo a massive, two-stage training regimen that imbues them with broad, foundational knowledge followed by specific, aligned behaviors.
Phase 1: Pre-Training – The Knowledge Ingestion
This is the most computationally intensive and costly phase, often requiring thousands of specialized AI accelerators (like GPUs or TPUs) running for weeks or months on terabytes of internet-scale text data. The objective is simple in formulation but profound in outcome: next-token prediction. The model is given a sequence of tokens (e.g., “The cat sat on the…”) and must predict the most probable next token (“mat”). It does this repeatedly across trillions of tokens spanning books, articles, code, and websites.
Through this self-supervised learning task, the model builds a statistical world model. It internalizes grammar, facts, reasoning patterns, and even stylistic nuances. The model’s parameters—weights and biases within its neural network—are adjusted via backpropagation and optimization algorithms like AdamW to minimize the difference between its predictions and the actual next tokens. The result is a foundational model: a highly capable but raw predictor that lacks instruction-following ability, safety guardrails, or specific task optimization.
Phase 2: Fine-Tuning and Alignment – Shaping Behavior
The raw, pre-trained model is a powerful but untamed predictor. It may generate toxic, biased, or irrelevant content. The fine-tuning phase shapes this raw capability into a helpful, harmless, and honest assistant. A key modern technique is Supervised Fine-Tuning (SFT), where the model is trained on high-quality, human-generated demonstrations of desired outputs (e.g., question-answer pairs, instruction-response dialogues).
The more revolutionary advancement is Reinforcement Learning from Human Feedback (RLHF), a multi-step process central to models like ChatGPT. First, human labelers rank multiple model outputs for a given prompt from best to worst. A separate reward model is trained to predict these human preferences. Then, the main LLM is fine-tuned using Proximal Policy Optimization (PPO) to generate outputs that maximize the reward model’s score, effectively aligning its text generation with complex human values like coherence, helpfulness, and safety. A third technique, Direct Preference Optimization (DPO), offers a more stable and efficient alternative to RLHF by directly using preference data to tune the model.
When a user prompts a trained LLM, the model performs inference. The input text is tokenized, converted into embeddings, and processed through the Transformer’s layers. The final output is a probability distribution over the model’s entire vocabulary (often 50,000 to 250,000+ tokens), known as a logits vector. This distribution represents the model’s “belief” about the next most likely token.
To generate text, this distribution is not simply maximized (taking the single highest-probability token, “greedy decoding”), as this can lead to repetitive, dull text. Instead, techniques like top-k sampling (choosing from the k most likely tokens) or nucleus (top-p) sampling (choosing from the smallest set of tokens whose cumulative probability exceeds p) introduce controlled randomness, fostering creativity and diversity. The temperature parameter further controls this randomness; a higher temperature flattens the distribution, making less likely tokens more probable.
This process is auto-regressive: the selected new token is appended to the input sequence, and the entire process repeats to generate the next token, continuing until a stop condition is met. This iterative nature explains why longer outputs take more time and computational resources to produce, as the model runs a full forward pass for each new token.
The scale of LLMs is defined by key parameters. Model size is typically measured by the number of parameters—the weights within the neural network that are adjusted during training. Models range from billions to trillions of parameters. The context window denotes the maximum sequence length (in tokens) the model can process at once, determining how much textual “conversation history” or document content it can consider when generating a response.
Training and running these behemoths require immense infrastructure. Hardware like NVIDIA’s H100 GPUs or Google’s TPU v5 pods are essential for their high-bandwidth memory and parallel processing capabilities. Frameworks like PyTorch and JAX facilitate the distributed training across thousands of these chips. A critical innovation enabling this scale is mixed-precision training, which uses 16-bit floating-point numbers for most calculations to speed up computation and reduce memory usage, while keeping key parts in 32-bit for stability.
Despite their power, LLMs have inherent limitations. They operate on statistical correlations, not true understanding or a grounded knowledge base, leading to potential hallucinations—confidently generating plausible but incorrect information. Their knowledge is static after pre-training, limited to their training data cutoff date unless augmented with external retrieval systems. Furthermore, they can perpetuate and amplify biases present in their training data, making ongoing research into robustness, fairness, and interpretability a critical frontier in the field.