LearnerBox Logo
LearnerBox Infosystems LLP

LLMs & NLP Glossary

Concepts related to large language models, natural language processing, and language understanding.

LLMs & NLP

📘

22 Terms

🔍

A–Z Linked

LLMs & NLP

Concepts related to large language models, natural language processing, and language understanding.

Select a term below or scroll through the full category.

Context

📘 Definition

Context is the surrounding information that helps an AI interpret the meaning of text or a conversation.

🪄 Simple Explanation

Context helps AI understand what you really mean.

🔑 Why It Matters

Providing context improves response quality.

💡 Example

Mentioning that an email is for a job application changes the AI's writing style.

Back to LLMs & NLP terms

Corpus

📘 Definition

A corpus is a large organised collection of text used to train or evaluate language models.

🪄 Simple Explanation

A corpus provides language examples for AI.

🔑 Why It Matters

High-quality corpora improve model performance.

💡 Example

Millions of books form a training corpus.

Back to LLMs & NLP terms

Decoder

📘 Definition

A decoder generates output tokens one at a time based on previous context.

🪄 Simple Explanation

The decoder produces the AI's response.

🔑 Why It Matters

Many language models use decoder architectures.

💡 Example

A decoder writes one word after another.

Back to LLMs & NLP terms

Dialogue System

📘 Definition

A dialogue system manages conversations between people and AI systems.

🪄 Simple Explanation

Dialogue systems support interactive communication.

🔑 Why It Matters

They underpin customer service and digital assistants.

💡 Example

A banking chatbot guides customers through account enquiries.

Back to LLMs & NLP terms

Encoder

📘 Definition

An encoder converts input text into meaningful internal representations that AI models can process.

🪄 Simple Explanation

An encoder helps AI understand input.

🔑 Why It Matters

Encoders are widely used in language understanding tasks.

💡 Example

A search model encodes documents before comparison.

Back to LLMs & NLP terms

Information Extraction

📘 Definition

Information extraction automatically identifies structured facts from unstructured text.

🪄 Simple Explanation

It turns text into structured information.

🔑 Why It Matters

It supports search and analytics.

💡 Example

Software extracts invoice numbers from scanned documents.

Back to LLMs & NLP terms

Intent

📘 Definition

Intent is the underlying purpose or goal behind a user's message.

🪄 Simple Explanation

Intent explains what the user wants to achieve.

🔑 Why It Matters

Intent detection is essential for chatbots and virtual assistants.

💡 Example

A support bot recognises that 'I can't log in' is a request for technical help.

Back to LLMs & NLP terms

Language Model

📘 Definition

A language model is an AI system trained to predict and generate human language by learning patterns from large collections of text.

🪄 Simple Explanation

A language model learns how words and sentences fit together.

🔑 Why It Matters

Language models are the foundation of modern conversational AI.

💡 Example

An AI assistant uses a language model to answer questions.

Back to LLMs & NLP terms

Large Language Model

📘 Definition

A Large Language Model (LLM) is an artificial intelligence model trained on vast amounts of text to understand, generate, summarise, translate, and respond to human language. Modern LLMs are typically based on transformer architectures and learn statistical patterns in language rather than memorising facts or following fixed rules.

🪄 Simple Explanation

A Large Language Model is an AI system that learns from enormous collections of text so it can understand and generate human language.

🔑 Why It Matters

Large Language Models power many of today's AI applications, including ChatGPT, Claude, Gemini, and Microsoft Copilot. Understanding LLMs provides the foundation for learning prompt engineering, conversational AI, and modern generative AI.

💡 Example

When you ask an AI assistant to explain a concept, write an email, summarise a report, or generate computer code, the response is typically produced by a Large Language Model.

Back to LLMs & NLP terms

Named Entity Recognition

📘 Definition

Named Entity Recognition (NER) identifies people, organisations, places, dates, and other entities within text.

🪄 Simple Explanation

NER finds important names in text.

🔑 Why It Matters

It helps organise and analyse information.

💡 Example

An AI extracts company names from news articles.

Back to LLMs & NLP terms

Natural Language Generation

📘 Definition

Natural Language Generation (NLG) is the process of producing human-like text from structured information or learned patterns.

🪄 Simple Explanation

NLG allows AI to write coherent text.

🔑 Why It Matters

It powers reports, summaries, and chatbots.

💡 Example

An AI writes a project summary from raw data.

Back to LLMs & NLP terms

Natural Language Understanding

📘 Definition

Natural Language Understanding (NLU) enables AI systems to interpret the meaning, intent, and context of human language.

🪄 Simple Explanation

NLU helps AI understand what people mean.

🔑 Why It Matters

It underpins conversational AI.

💡 Example

A chatbot identifies that a customer wants a refund.

Back to LLMs & NLP terms

Open-weight AI

📘 Definition

Open-weight AI refers to artificial intelligence models whose trained model weights are publicly available for others to download, run, fine-tune, or deploy. While the weights are accessible, the model may not be fully open source because its training data, code, or licence may still have restrictions.

🪄 Simple Explanation

Open-weight AI models let people use and customise the model because the trained weights are publicly available.

🔑 Why It Matters

Open-weight models have accelerated AI research, education, and commercial innovation by allowing developers and researchers to run powerful models on their own hardware, fine-tune them for specialised tasks, and build new applications without relying entirely on cloud-based proprietary services. However, having open weights does not necessarily mean the model is fully open source.

💡 Example

A university downloads an open-weight language model and fine-tunes it on medical research papers to create a specialised assistant for healthcare researchers.

Back to LLMs & NLP terms

Question Answering

📘 Definition

Question answering enables AI to answer questions using learned knowledge or retrieved information.

🪄 Simple Explanation

AI answers questions in natural language.

🔑 Why It Matters

It powers many virtual assistants.

💡 Example

A student asks AI to explain Newton's laws.

Back to LLMs & NLP terms

Semantic Search

📘 Definition

Semantic search retrieves information based on meaning rather than exact keyword matches.

🪄 Simple Explanation

Semantic search understands intent.

🔑 Why It Matters

It produces more relevant search results.

💡 Example

Searching for 'car' also finds documents about automobiles.

Back to LLMs & NLP terms

Sentiment Analysis

📘 Definition

Sentiment analysis determines whether text expresses positive, negative, or neutral opinions.

🪄 Simple Explanation

It measures the emotional tone of text.

🔑 Why It Matters

Businesses use it to understand customer feedback.

💡 Example

Reviews are classified as positive or negative.

Back to LLMs & NLP terms

Sequence

📘 Definition

A sequence is an ordered series of tokens or data processed by a language model.

🪄 Simple Explanation

Language models analyse sequences of tokens.

🔑 Why It Matters

Order is important in language understanding.

💡 Example

Changing word order changes sentence meaning.

Back to LLMs & NLP terms

Similarity

📘 Definition

Similarity measures how closely two pieces of information are related.

🪄 Simple Explanation

Similarity helps AI compare meaning.

🔑 Why It Matters

It improves recommendation and search systems.

💡 Example

An AI finds articles similar to a research paper.

Back to LLMs & NLP terms

Text Classification

📘 Definition

Text classification assigns documents or messages to predefined categories.

🪄 Simple Explanation

It automatically labels text.

🔑 Why It Matters

It supports spam detection and document organisation.

💡 Example

Emails are classified as spam or legitimate.

Back to LLMs & NLP terms

Tokenisation

📘 Definition

Tokenisation is the process of splitting text into tokens before it is processed by a language model.

🪄 Simple Explanation

Tokenisation prepares text for AI.

🔑 Why It Matters

It is the first stage of language processing.

💡 Example

A sentence is divided into tokens before inference.

Back to LLMs & NLP terms

Vector

📘 Definition

A vector is a numerical representation of information used by AI models to compare meaning mathematically.

🪄 Simple Explanation

Vectors convert information into numbers.

🔑 Why It Matters

Vectors enable semantic similarity.

💡 Example

Similar sentences have nearby vectors.

Back to LLMs & NLP terms

Vocabulary

📘 Definition

A model's vocabulary is the collection of tokens it recognises during language processing.

🪄 Simple Explanation

Vocabulary defines what a model can represent.

🔑 Why It Matters

It influences tokenisation and efficiency.

💡 Example

Frequently used words often become single tokens.

Back to LLMs & NLP terms

Explore More AI Learning Resources

Continue learning with LearnerBox AI guides, glossary entries, and practical learning pathways.

Back to AI Glossary Explore AI Guides