What are large language models? A technical breakdown
Explore the technical foundations of large language models (LLMs), including how they work, their architecture, training methods, and real-world applications
Large language models (LLMs) have become a cornerstone of modern artificial intelligence (AI), powering everything from conversational AI systems like ChatGPT to advanced search engines and developer tools.
If you're wondering "what is an LLM?" or want to explore how these models work, their architecture, and their real-world applications, this technical breakdown will provide the answers.
What are large language models?
Large language models (LLMs) are advanced AI systems designed to understand and generate human-like text.
These models leverage vast datasets and billions (or even trillions) of parameters to perform a variety of natural language processing (NLP) tasks, such as text generation, summarization, translation, and more.
What are the key characteristics of LLMs?
Scale: LLMs are "large" because they have billions of parameters, which are the internal weights that help the model learn language patterns.
Self-supervised learning: Instead of relying on labeled datasets, LLMs are trained on massive, unstructured text corpora using self-supervised learning. This allows them to learn patterns, grammar, facts, and context from raw text.
Versatility: Unlike traditional AI models designed for single tasks, LLMs can adapt to a wide range of tasks without requiring task-specific training.
For example, ChatGPT, built on GPT (Generative Pre-trained Transformer) architecture, can perform tasks like answering questions, providing recommendations, writing code, and even generating creative text, all powered by LLM technology.
How do large language models work?
At their core, LLMs predict the next word or token in a sequence, enabling them to generate coherent and contextually relevant text.
This process involves several steps:
Data collection
LLMs are trained on diverse datasets that include web pages, books, articles, and even code repositories.
This diversity ensures the model can handle various language styles, topics, and domains.
For instance:
Training datasets may consist of hundreds of billions of words or tokens.
The inclusion of different forms of text (e.g., dialogues, academic papers, and code) equips LLMs to perform a wide range of tasks.
Tokenization
Before processing text, LLMs break it into smaller units called tokens. Tokens can be words, subwords, or even individual characters.
For example:
The sentence "Large language models are powerful" might be tokenized as:
["Large", "language", "models", "are", "powerful"].
Tokenization helps the model process text efficiently, allowing it to understand language structure.
Training objectives
LLMs are trained using two primary objectives:
Masked language modeling (MLM): The model predicts missing words in a sentence (e.g., "The cat sat on the ___").
Causal language modeling (CLM): The model predicts the next word in a sequence (e.g., "The cat sat on the").
Think of it like a supercharged version of autocomplete, where the model learns to generate text by predicting what comes next based on context.
Fine-tuning
After pretraining, LLMs can be fine-tuned for specific use cases:
Instruction tuning: Helps the model follow user instructions more effectively.
Reinforcement learning from human feedback (RLHF): Uses feedback from humans to improve the quality, safety, and relevance of the model's outputs.
For example, fine-tuning allows LLMs to generate more accurate code suggestions or provide domain-specific answers in healthcare or law.
Important note that there are also differences between cloud llms and local llms.
Local llms are gaining certain traction because they’re believed to be “more secure”.
Though some devs go with cloud llms that are as popular in the dev community.
Key architecture and training methods
Modern LLMs are built on transformer architecture, a revolutionary framework introduced in 2017. Transformers allow models to process large amounts of text efficiently while maintaining contextual understanding.
Transformer fundamentals
The transformer architecture consists of:
Encoders: Process input text.
Decoders: Generate output text.
Self-attention mechanisms: Enable the model to focus on relevant parts of the input when interpreting meaning.
This architecture allows LLMs to process entire sentences (or even paragraphs) in parallel, rather than word by word, making them faster and more contextually aware.
Attention mechanisms
Self-attention helps LLMs focus on important parts of a sentence.
For instance:
In the sentence "The cat sat on the mat because it was soft", the model uses attention to link "it" to "mat".
Think of attention as a highlighting tool that helps the model prioritize the most relevant words.
Scaling laws
Research shows that larger models trained on more data perform better.
However, this improvement follows a power law, meaning that while performance increases, the cost and resources required grow exponentially.
LLM use cases for developers
Large language models are incredibly powerful tools for developers, offering practical applications that enhance productivity:
Workflow automation
LLMs automate repetitive tasks like:
Generating boilerplate code.
Formatting and refactoring code.
Creating unit tests for new functions.
Code assistance
LLMs provide real-time code suggestions and explanations, helping developers write better code faster.
Tools like Pieces, alongside with other free and paid ai tools for developers, integrate LLMs to streamline workflows by offering context-aware recommendations within coding environments.
3. Documentation generation
LLMs can generate high-quality documentation, including:
API documentation.
Inline comments.
Technical writing for complex systems.
Benefits and challenges of LLMs
Benefits
Efficiency: LLMs save time by automating mundane tasks and retaining context across sessions.
Flexibility: They can handle a wide range of tasks from text generation to code analysis.
Challenges
Bias and ethics: LLMs can reflect biases in their training data, leading to biased outputs.
Resource constraints: Training and running LLMs require significant computational power.
Hallucination: LLMs sometimes generate plausible-sounding but incorrect information.
Future outlook for large models
Ready for What’s Next in AI?
The future of large models is evolving fast. Whether it's multimodal capabilities blending text, images, and code, leaner models optimized for your device, or safer, smarter outputs, we're entering a new era of AI innovation.
👉 Curious about how small language models (SLMs) differ from large ones? Read this article to dive deeper into the distinctions and discover which is right for your use case.
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