What Are Large Language Models? How They Work, Training, and Potential Applications of Large Language Models

Large language models are based on neural networks, an AI architecture modeled after the functioning of the human brain. In the brain, interconnected neurons communicate via electrical impulses and chemical signals. In neural networks, software nodes take on this role and communicate through mathematical calculations.

The specific architecture used by modern LLMs is called a Transformer. Transformers were introduced in 2017 in the groundbreaking paper “Attention Is All You Need” and have revolutionized AI-driven language processing. Their key innovation is the attention mechanism, also known as self-attention: a process that enables the model to compare the meanings of different words in a sentence relative to one another, thereby capturing contextual relationships across long stretches of text.

Here’s a concrete example: In the sentence “John hit the ball. He was fast,” the attention mechanism recognizes that “he” refers to “John” and not to “ball” because it evaluates the relationships between all tokens in the sequence simultaneously.

Transformers also enable parallel processing of computations, which is what makes training on massive amounts of data feasible in the first place. Earlier architectures, such as Recurrent Neural Networks (RNNs), processed text sequentially, word by word. Transformers can process all tokens in a sequence simultaneously, which speeds up training significantly.

The processing of a text by a large language model follows a clearly defined four-step process:

Step 1: Tokenization. The input text is broken down into smaller units called tokens. A token can be an entire word, part of a word, or even a single character. Current LLMs predominantly use partial-word methods such as Byte-Pair Encoding, which offer a balance between vocabulary size and semantic information content. For example, the sentence “The sun rises” is broken down into the following token IDs:

Step 2: Embedding. Each token is converted into a mathematical vector—a sequence of numbers that represents the token’s meaning in the space. Semantically similar words such as “sun” and “moon” are closer to each other in the vector space than “sun” and “shoes.” In addition, positional encodings are added, which provide the model with information about the position of each token in the sequence.

Step 3: Predicting the next token. This is the core of the LLM. The embeddings pass through the Transformer layers, where the attention mechanism evaluates the relationships between all tokens. Each embedding is projected into three vectors: a query vector (what the token is “looking for”), a key vector (what information the token contains), and a value vector (what information is returned). Attention weights are calculated based on the similarities between queries and keys; these weights determine how strongly different tokens influence the prediction. Finally, the model calculates probabilities for all possible next tokens.

Step 4: Decoding. A decoding strategy determines which token is actually output based on the calculated probabilities. The simplest approach (greedy search) always selects the most probable token. More complex methods, such as top-K sampling, select from the k most probable tokens; beam search evaluates entire token sequences; and contrastive search balances probability against similarity to the context so far.

This process is repeated token by token until the complete output is generated. The model does not “know” the final answer in advance. At each step, it makes its statistically best prediction.

Training a large language model is a multi-stage process that requires significant resources, large amounts of data, and expertise.

Stage 1: Self-Supervised Pre-training

In the first phase, the model is fed vast amounts of raw data: websites, books, scientific articles, forum posts, programming code, and millions of conversations. The model does not “read” these texts to acquire knowledge, but rather for pattern recognition. It learns to predict missing parts and thereby develops a statistical understanding of language structure, grammar, and context.

This phase is extremely computationally intensive. It requires thousands of GPUs, weeks to months of computation time, and correspondingly high energy costs.

Stage 2: Supervised Fine-Tuning

In the second phase, the pre-trained model is further trained using a smaller, labeled dataset. The model learns to respond to specific instructions and interact with users. While pre-training provides the model with a broad understanding of language, fine-tuning makes it usable for specific tasks such as question-answering, summarization, or conversation.

Fine-tuning can also be domain-specific: A model trained on general knowledge can be adapted using medical, legal, or technical texts so that it understands specialized terminology and provides industry-specific answers.

Stage 3: Reinforcement Learning from Human Feedback (RLHF)

In the third phase, human annotators evaluate the model’s outputs and distinguish between good and bad answers. Based on this feedback, a reward model is trained to promote desired behaviors and suppress undesirable outcomes. RLHF is particularly important for reducing harmful, offensive, or misleading outputs and improving the quality of responses.

This phase also includes what is known as alignment: the process of making LLM outputs useful and safe and bringing them into alignment with human values.