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Advanced Topic 38 of 51 AI & Machine Learning

Large Language Models

Huge transformers trained to predict and generate text.

How it works

Large language models are transformer-based neural networks with billions of parameters, trained on massive text corpora to predict the next token. This simple objective leads to broad abilities such as writing, summarizing, translating, reasoning and coding. Pretraining is followed by instruction tuning and reinforcement learning from human feedback to make models follow instructions and be helpful and safer. Prompting, in-context learning, retrieval augmentation and tool use extend their capabilities without retraining.

Step-by-step walkthrough

  1. A transformer is trained on trillions of words.
  2. It learns to predict the next token.
  3. Fine-tuning and human feedback make it follow instructions.
  4. Users prompt it to write, summarize or code.
  5. It may hallucinate, so verify important facts.

Where it is used

Chat assistants, coding tools, search, customer support, document analysis, drafting and data extraction use LLMs.

Common pitfall

They can hallucinate plausible but false statements, reflect biases in training data, be manipulated through prompt injection and have knowledge cutoffs. They are costly to train and run, with context length limits. Verify important outputs, ground responses with retrieval, evaluate on task-specific tests, protect sensitive data and design for failures.

Where it fits in the course

Large Language Models is topic 38 of 51 in AI & Machine Learning, rated Advanced. It follows Transformers. The next topic is Embeddings.

Practice and explore

How to study this topic

  1. Read the explanation above and restate the definition in your own words.
  2. Open the interactive lesson and answer its mini quiz.
  3. Read the common pitfall, then write a short example that avoids it.
  4. Check your understanding in the AI & Machine Learning quiz, and revisit the topic if you miss a question.

Frequently asked questions

What is Large Language Models?
Huge transformers trained to predict and generate text. It belongs to AI & Machine Learning and is an advanced-level topic.
Where do people use Large Language Models?
Chat assistants, coding tools, search, customer support, document analysis, drafting and data extraction use LLMs.
What is the most common mistake with Large Language Models?
They can hallucinate plausible but false statements, reflect biases in training data, be manipulated through prompt injection and have knowledge cutoffs.
What should I learn before this topic?
Start with Transformers, the topic just before it in AI & Machine Learning.
How can I practice it?
Answer the mini quiz in the interactive lesson, then take the AI & Machine Learning quiz.
Is Large Language Models hard to learn?
It is an Advanced topic. Expect to read the explanation more than once and to connect it with earlier topics. Working through a small example by hand helps most.
How long does it take to learn Large Language Models?
Most learners can read the explanation and finish the interactive lesson in under an hour. Fluency comes from applying the idea, so plan a few short practice sessions with the quiz.
Which topics are related to Large Language Models?
Transformers, Embeddings, Attention Mechanism are closely connected. The full list is in the related topics section of this page.
What should I study after Large Language Models?
Continue with Embeddings, the next topic in AI & Machine Learning.

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