Large Language Models (LLMs): What They Are and How They Work
- Samul Black
- Dec 17, 2024
- 13 min read
Updated: Sep 17
Large Language Models (LLMs) have revolutionized natural language processing (NLP) and artificial intelligence (AI). From powering chatbots to generating human-like text, LLMs like GPT, BERT, and LLaMA are at the forefront of innovation.
In this blog, we’ll explore what LLMs are, how they work, their applications, and the challenges they present.
What Are Large Language Models?
Large Language Models (LLMs) are a groundbreaking class of artificial intelligence systems capable of understanding and generating human language. Trained on massive datasets sourced from books, websites, research papers, and social media, LLMs learn the complex patterns, structures, and semantics of natural language.
By predicting the next word in a sequence based on the surrounding context, these models can perform an impressive range of language-related tasks — from writing essays and generating code to answering questions, translating languages, and even reasoning across modalities like images and audio.
LLMs power many of the most advanced AI systems in use today — including virtual assistants, chatbots, recommendation engines, and content generation tools.
Key Characteristics of Large Language Models
To understand why LLMs are so effective, it’s helpful to break down their defining features:
Scale - Modern LLMs are massive in size, with millions to hundreds of billions of parameters — the internal values learned during training that enable the model to capture complex linguistic patterns. The larger the model, the better it typically performs on diverse and nuanced tasks.
Generalization - One of the most powerful aspects of LLMs is their ability to generalize across tasks. Once pre-trained, a single model can adapt to sentiment analysis, summarization, question answering, code completion, and many other tasks — often without needing task-specific training.
Context Awareness - LLMs can analyze and understand large chunks of text, capturing meaning, tone, and intent. This contextual understanding allows them to generate coherent responses, follow conversations, and handle multi-turn dialogues more effectively than previous NLP models.
Multimodal Capabilities (in newer models) - Some of the latest LLMs, like Google’s Gemini or OpenAI’s GPT-4o, are multimodal, meaning they can process and integrate information across different formats — such as text, code, images, audio, and video — enabling more comprehensive and intelligent interactions.
Few of the Popular Large Language Models
Let’s explore some of the most influential and widely adopted LLMs shaping today’s AI landscape:
1. GPT (Generative Pre-trained Transformer)
GPT models (e.g., GPT-3, GPT-4, GPT-4o) are some of the most well-known LLMs in use today. They’re designed to generate fluent, creative, and contextually accurate text, making them ideal for chatbots, content writing, coding assistants, and more. Few strengths of these are:
Autoregressive transformer architecture
Exceptional at text generation and conversation
Strong performance in few-shot and zero-shot tasks
GPT-4o introduces multimodal capabilities (text, vision, and audio)
2. BERT (Bidirectional Encoder Representations from Transformers)
BERT revolutionized NLP by introducing a bidirectional transformer model that processes text from both directions simultaneously. Unlike GPT, BERT is primarily used for understanding text rather than generating it. Main strengths of these are:
Bidirectional context comprehension
Powers Google Search and many enterprise NLP solutions
Excellent for classification, sentence matching, and Q&A
3. Gemini
Gemini is Google’s next-generation multimodal AI model, designed to outperform previous models by combining capabilities across text, images, audio, video, and code. It represents a leap beyond the limitations of single-modality language models. Few strengths of these are:
Unified multimodal reasoning (text, images, and code)
Strong performance in logic, mathematics, and coding
Designed for real-world reasoning and intelligent assistance
4. LLaMA (Large Language Model Meta AI)
LLaMA models are open-weight LLMs optimized for efficiency. They are particularly popular in academic and open-source communities due to their lightweight architecture and strong performance without excessive computational requirements. Few strengths of these are:
Efficient for training and deployment on modest hardware
Widely used in fine-tuned, domain-specific models
Core engine for many open-source alternatives to GPT
5. Claude
Claude models prioritize harmlessness, honesty, and helpfulness, with a focus on safety. They are designed to follow human-aligned principles and offer strong performance in reasoning and instruction-following tasks. Few strengths of these are:
Constitutional AI approach to alignment and safety
Excellent in multi-step reasoning and long-context tasks
Large Language Models have transformed the field of artificial intelligence by enabling machines to understand and generate human language at an unprecedented scale and fluency. From powering virtual assistants and chatbots to supporting research, education, and business automation, LLMs are reshaping how we interact with technology. With ongoing innovation in multimodal reasoning, contextual understanding, and ethical alignment, LLMs like GPT, Gemini, BERT, and LLaMA are not just tools — they are the backbone of the next wave of intelligent systems.
Main Components of Large Language Models (LLMs)
Large Language Models are built using sophisticated deep learning architectures. Their remarkable ability to understand and generate human-like text relies on several interconnected components. Here’s a breakdown of the key building blocks:
1. Tokenizer: The First Step in Large Language Models
Before any neural processing begins, raw text must be converted into a numerical format that the model can understand. This conversion is handled by the tokenizer, one of the most essential — yet often overlooked — components in any Large Language Model (LLM) pipeline.
A tokenizer is a preprocessing tool that breaks down input text into smaller units called tokens, and maps those tokens to unique numerical identifiers known as token IDs. These token IDs are then passed to the model for further processing.
In LLMs, tokenization enables models to handle an open-ended vocabulary using a manageable and consistent input format. The tokenizer typically performs two main steps:
Text → Tokens
The input text is split into tokens using rules defined by the tokenizer (e.g., splitting by words, subwords, or characters).
Tokens → Token IDs
Each token is mapped to a corresponding ID using a pre-built vocabulary.
For Example (GPT-style tokenizer): Input:
"Large Language Models"→ Tokens: ["Large", " Language", " Models"]→ Token IDs: [10234, 2456, 8761](Spaces may be preserved depending on the tokenizer's rules)
Different LLMs use different tokenization strategies based on their architecture and training goals.
1. Byte Pair Encoding (BPE) — Used in GPT, LLaMA
Splits words into subword units based on frequency.
Helps handle out-of-vocabulary words by composing them from common subword pieces.
Efficient for large vocabularies with fewer unknown tokens.
2. WordPiece — Used in BERT
Similar to BPE, but builds subwords using a greedy longest-match-first algorithm.
Performs well in understanding tasks by maintaining consistency in tokenization.
3. SentencePiece (Unigram/BPE) — Used in T5, Gemini, PaLM
Treats input as raw text without relying on whitespace or punctuation.
Enables multilingual and cross-domain consistency.
Can use a unigram model or BPE under the hood.
4. Character-Level Tokenization (rare in LLMs, but used in some domains)
Each character becomes a token.
Useful for languages without word boundaries or for specific tasks like code modeling.
Tokenization isn't just a preprocessing step — it affects everything the model learns and generates:
Vocabulary Coverage: Good tokenization reduces the number of unknown tokens.
Context Efficiency: Better subword splits mean more tokens fit within limited context windows (e.g., 4K, 8K, 32K tokens).
Semantic Clarity: Logical token boundaries help the model learn more coherent representations.
Vocabulary Size and Trade-Offs
Tokenization in Practice: Example with GPT
from transformers import GPT2Tokenizer
tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
text = "Language models are powerful."
tokens = tokenizer.tokenize(text)
token_ids = tokenizer.convert_tokens_to_ids(tokens)
print("Tokens:", tokens)
print("Token IDs:", token_ids)
Output:
Tokens: ['Language', 'Ġmodels', 'Ġare', 'Ġpowerful', '.']
Token IDs: [32065, 4981, 389, 3665, 13]2. Embedding Layer in Large Language Models
After tokenization converts input text into a sequence of numerical token IDs, the next critical step is the Embedding Layer. This layer transforms those token IDs into dense, continuous vectors that can be processed by the neural network.
In essence, the Embedding Layer serves as the LLM’s vocabulary lookup table, where each word, subword, or token is mapped to a vector representation capturing its meaning and usage patterns.
An embedding is a fixed-size vector (typically 256 to 4096 dimensions) representing a token in a continuous vector space. Unlike raw token IDs, embeddings enable the model to work with semantic information — relationships, analogies, and contextual meanings between words. Embeddings work typically in the following fashion:
Input: A sequence of token IDs, e.g. [2023, 4301, 389, 9051, 13]
Lookup: Each ID is used to retrieve a corresponding embedding vector from an embedding matrix.
Output: A matrix of shape [sequence_length × embedding_dim] — this becomes the input to the transformer layers.
For example:
Text Input → "Language models are powerful."
Token IDs → [2023, 4301, 389, 9051, 13]
Embedding Layer→ [[0.12, -0.43, ..., 0.09], ← "Language"
[0.34, 0.01, ..., -0.55], ← "models"
...
]The purpose of the embedding layer include:
Semantic Representation: Embeddings capture meaning, similarity, and relationships between tokens.
Dimensionality Reduction: Converts large sparse vocabularies into smaller dense vectors.
Generalization: Helps the model handle unseen text by grouping semantically similar tokens near each other in vector space.
The embedding dimension determines how much information the model can store about each token. Common sizes:
Larger embedding sizes can represent more complex features, but also increase memory and computation costs. Many LLMs (e.g. GPT) share the embedding matrix with the final output (softmax) layer. This technique reduces the number of parameters and improves learning efficiency.
3. Positional Encoding in Large Language Models
After converting text into token embeddings, LLMs still need a way to understand the order of tokens in a sequence — because word order deeply affects meaning in language.
Unlike RNNs or LSTMs, transformer-based models (like GPT, BERT, Gemini, etc.) do not process input sequentially. They look at the entire input at once (in parallel), which is powerful for performance, but introduces a challenge:
Transformers have no inherent sense of order — they must be explicitly taught where each word appears in a sequence.
Positional encoding injects information about each token’s position in the sequence into its vector representation, allowing the model to distinguish between, say:
“The cat chased the dog”
“The dog chased the cat”
Without positional encoding, both would look nearly identical to the model, because the embeddings alone don’t contain ordering information.
The model adds or concatenates a positional vector to each token embedding. This positional vector encodes the token’s index in the sequence — either via a mathematical function or learned parameters.
There are two main types of positional encodings:
1. Fixed (Sinusoidal) Positional Encoding — Used in original Transformer paper
Introduced by Vaswani et al. (2017), this approach uses sine and cosine functions of different frequencies to represent positions:
PE(pos,2i) = sin(pos/10000^2i/d)
PE(pos,2i + 1) = cos(pos/10000^2i/d)
pos = token position
i = dimension index
d = embedding dimension
Benefits:
No additional parameters to train
Generalizes to longer sequences than seen during training
2. Learned Positional Embedding — Used in GPT, BERT, Gemini, etc.
Instead of a fixed formula, this approach uses a trainable embedding vector for each possible position in the input.
Just like token embeddings, these are learned during model training.
The model adapts the position encodings to match real linguistic patterns.
Benefits:
Often leads to better performance in practice
More flexible and adaptable to specific tasks and datasets
Combined Input: Token + Positional Embeddings
Once positional encodings are generated, they are typically added element-wise to the token embeddings
Final Input = Token Embedding + Positional EncodingThis combined vector is then passed to the first transformer block for further processing.
Let’s say you have the input: "The cat sat"
Positional Encoding in Different Models
Positional encoding is essential in large language models because it enables them to understand the order of words, which is crucial for interpreting meaning—for example, “He ate after she left” conveys a different scenario than “She left after he ate.” Without positional information, the model would treat both sentences the same. By embedding token positions into the input, positional encoding allows the attention mechanism to operate in a context-aware manner, improving the model's ability to generate coherent responses in long sequences such as paragraphs, documents, or lines of code.
Key Takeaways
4. Transformer Blocks
Once the text has been tokenized, embedded, and enriched with positional information, it enters the transformer blocks—the powerhouse of large language models. These blocks are repeated many times in deep architectures and are responsible for the bulk of the model’s ability to understand, reason, and generate language.
A transformer block is made of three main pillars: the multi-head self-attention mechanism, the position-wise feed-forward network, and the residual connection with normalization layers. Let’s break these down in detail.
1. Multi-Head Self-Attention (MHSA)
This is the most revolutionary part of the transformer design, replacing older recurrent networks by allowing the model to look at the entire sequence at once. Key functions of this block are :
Contextual Understanding: For each word, the model can “attend” to other words in the sequence, regardless of their distance.
Parallel Processing: Since all tokens are processed together, the model is faster and more scalable than RNNs.
How It Works:
Every token representation is projected into three vectors:
Query (Q) – what this word is looking for.
Key (K) – what this word offers as context.
Value (V) – the actual content to be passed along.
The attention score between tokens is computed as:
Attention(Q,K,V)=softmax(QK^T/SqRootdK)V
Multi-head means this attention calculation is done in parallel across multiple sets of Q, K, and V vectors, each learning a different “type” of relationship—syntax, semantic meaning, topic flow, etc.
Example:In "The book on the table is mine", one attention head may link “book” with “mine,” while another may capture that “on the table” is a descriptive phrase tied to “book.”
2. Position-Wise Feed-Forward Network (FFN)
Once the attention mechanism has fused contextual information into each token’s representation, the FFN applies a transformation to refine it.
Structure:
Two linear layers with a ReLU (or GELU) activation in between.
Applied independently to each token but with the same parameters across the sequence.
Purpose:
Increases the representational capacity of the model.
Allows complex feature transformations beyond attention’s mixing of information.
3. Residual Connections and Layer Normalization
Deep networks can suffer from vanishing gradients or lose information as data passes through multiple layers. Transformer blocks address this with:
Residual (Skip) Connections: Each sub-layer (attention and feed-forward) has its output added to its original input before passing forward. This preserves the original signal while adding new learned features.
Layer Normalization: Normalizes the input to each sub-layer, stabilizing training and improving convergence speed.
The Flow Inside a Transformer Block
Input from the previous block (or embeddings layer) enters the multi-head self-attention sub-layer.
Residual connection adds the original input to the attention output.
Layer normalization ensures stable feature scaling.
Output then enters the feed-forward network.
Another residual connection and layer normalization are applied.
Result is passed to the next transformer block in the stack.
In large language models, transformer blocks are stacked dozens or even hundreds of times, with billions of parameters spread across them. These stacks allow the model to form multi-level abstractions—from basic grammar in early layers to reasoning and domain-specific knowledge in deeper layers.
5. Output Head (Language Modeling Head)
After passing through the transformer layers, the model produces a contextualized vector for each token position in the sequence. The Output Head, sometimes called the Language Modeling Head, is responsible for turning these vectors into actual predictions.
Function: It applies a linear transformation to map each vector into a set of logits — one for every token in the vocabulary — and then uses the softmax function to convert these logits into a probability distribution.
Purpose: This allows the model to determine which token is most likely to come next, given the preceding context.
Example: If the context is “The cat sat on the”, the output head might assign the highest probability to the token “mat”, followed by alternatives like “sofa” or “floor”.
Application: Used in both training and inference to choose the most probable next token in language generation or prediction tasks.
6. Loss Function
Training a large language model requires a clear way to measure how far off its predictions are from the ground truth. This is where the Loss Function comes in — most commonly, cross-entropy loss in language modeling.
Function: Compares the predicted probability distribution from the output head with the actual token (represented as a one-hot vector).
Goal: Minimize this loss during training so that the model’s predictions become increasingly accurate.
Training Dynamics: The loss is backpropagated through all layers of the network, adjusting millions (or billions) of parameters to improve future predictions.
7. Training Data & Objective
While not a direct component of the neural architecture, the training data and objective fundamentally shape what a large language model can do.
Training Data:LLMs are trained on massive and diverse datasets, often containing:
Web pages, articles, and blogs
Books and academic papers
Wikipedia and encyclopedic resources
Code from repositories
Forum discussions and Q&A sites
In multimodal models like Gemini, images, videos, and other media
Training Objectives:
GPT-style (Autoregressive): Predict the next token in a sequence, one at a time.
BERT-style (Masked Language Modeling): Fill in randomly masked words in a sentence.
Gemini-style (Multimodal): Learn from multiple data types simultaneously — text, images, code — enabling richer reasoning and cross-modal understanding.
These choices — what data is included, how it’s cleaned, and what objective is used — directly affect an LLM’s capabilities, biases, and domain expertise.
Summary Table: Components of LLMs
Conclusion
Large Language Models are more than just massive collections of parameters — they’re carefully engineered systems with multiple specialized components working together. From tokenization to embeddings, positional encodings, transformer blocks, and finally the output head, every stage plays a critical role in understanding and generating human language. The loss function guides their learning, while diverse training data shapes their versatility.
Whether it’s GPT generating natural-sounding dialogue, BERT excelling at text understanding, or Gemini fusing multiple modalities, these architectures have redefined how we interact with AI — powering chatbots, content creation tools, coding assistants, and research applications on a global scale.
