NLP and Large Language Models
What is NLP?
Natural language processing (NLP) combines artificial intelligence, computer science and linguistics to help computers understand, analyze and generate human language.
Reading a sentence takes little conscious effort for people, but computers see digital signals. Without suitable algorithms, a computer cannot tell whether “apple” refers to a fruit or the company. NLP develops methods for interpreting the meaning of text.
- Machine translation: translate Chinese into English, Japanese and other languages.
- Search engines: understand queries and find the most relevant results.
- Spam filtering: identify unwanted email.
- Sentiment analysis: classify reviews as positive, negative or neutral.
- Chatbots: hold natural conversations with users.
What is an LLM?
A large language model (LLM) is a major development in NLP. While NLP studies how computers understand language, LLMs are language models trained on large datasets with extensive model capacity.
Its core ability is predicting the most likely next text from preceding context. Given “The weather today is very”, it may predict “nice”, “hot”, “cold” or “clear”, then continue until it forms a complete answer. This resembles a very fast word-completion process.
How has it developed?
By 2026, generative AI has gained a global user base. Enterprise adoption of LLMs and other generative AI technologies may create substantial economic value while changing information organization, content generation and human-computer interaction.
Why use it?
Much of everyday work involves reading, organizing, searching, writing and communicating: extracting report highlights, organizing meeting notes, replying to email, consulting technical documents or turning an idea into a proposal.
LLMs interpret text and generate responses in context. Describe your goals, supply information and set constraints to get help organizing information, drafting, comparing options, explaining concepts or checking content.