Is NLP the same as large language models?

No — NLP is the broad field; LLMs are the current dominant tool within it. Plenty of NLP work still uses lighter, task-specific methods where they are faster or cheaper.

What are common real-world uses?

Search, machine translation, sentiment analysis, chat assistants, document summarisation and information extraction — most products that read or write text lean on NLP somewhere.

What is Natural Language Processing (NLP)?

Natural Language Processing

Also: NLP, computational linguistics, language AI

Natural language processing is the field of AI concerned with getting computers to work with human language — understanding it, generating it, translating it and pulling meaning from it.

Teaching machines to handle words

Human language is messy — ambiguous, context-dependent, full of exceptions — which makes it one of the hardest things to compute over. NLP covers the techniques that cope with it: sorting text by topic, extracting names and facts, gauging sentiment, translating between languages, answering questions and summarising. Search engines, spam filters, voice assistants and autocomplete are all everyday NLP.

The field was transformed by deep learning and then by the Transformer: tasks that once needed bespoke systems are now often handled by a single large language model. NLP is effectively the language wing of modern AI, the way computer vision is its eyes.

Key takeaways

→NLP is AI applied to human language — classification, extraction, translation, generation and more.
→Transformers and LLMs collapsed many once-separate NLP tasks into one general approach.

Related concepts

Broader topicMachine Learning
NLP is an application area of machine learning.
RelatedLarge Language Models
LLMs now power most natural-language-processing tasks.

FAQ

Is NLP the same as large language models?: No — NLP is the broad field; LLMs are the current dominant tool within it. Plenty of NLP work still uses lighter, task-specific methods where they are faster or cheaper.
What are common real-world uses?: Search, machine translation, sentiment analysis, chat assistants, document summarisation and information extraction — most products that read or write text lean on NLP somewhere.

Natural Language Processing

Teaching machines to handle words

Key takeaways

Related concepts

Courses that teach this

FAQ