AdvancedTopic 98 of 104Software & Computing
Natural language processing and LLMs
Transformers model language and power modern assistants.
In depth
NLP processes text: tokenisation, embeddings, sequence models. Transformers with self-attention power large language models (LLMs) trained on huge corpora to predict next tokens, then aligned with human feedback. Applications: chatbots, translation, summarisation, code assistants. Limits: hallucination, bias, cost, context length.
Key points
- Attention mechanism
- Prompting and RAG
- Evaluation and safety
ExampleA customer support assistant retrieves documents and answers with an LLM.
Language models process and generate text, so they are now central to assistants, search, coding help and document work.
What is Natural language processing and LLMs?
Transformers model language and power modern assistants.
Why is it important to understand natural language processing and LLMs?
Language models process and generate text, so they are now central to assistants, search, coding help and document work.
What is a transformer?
An architecture that uses attention to relate every token to others in a sequence, which allows efficient training on large text.
What are the main risks of language models?
They can produce plausible but wrong statements, reflect bias in data, and reveal private information if not handled carefully.
How is Natural language processing and LLMs used in practice?
Typical applications include summarising a long technical report, drafting a reply to a customer email for review and answering questions over a set of manuals using retrieval.
What should I learn before Natural language processing and LLMs?
Study "Deep learning" first (topic 97 of 104 in Software & Computing). It covers the groundwork this topic builds on.
What are the key points of Natural language processing and LLMs?
Attention mechanism. Prompting and RAG. Evaluation and safety.
Can you give an example of Natural language processing and LLMs?
A customer support assistant retrieves documents and answers with an LLM.
What comes after Natural language processing and LLMs?
The next topic is "Computer vision", which builds directly on this one.
How difficult is Natural language processing and LLMs?
It is classed as Advanced in Software & Computing (topic 98 of 104). It is an advanced topic. It assumes you are comfortable with the intermediate topics in Software & Computing.