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LLMs & NLP Glossary

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

LLMs & NLP

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24 Terms

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A–Z Linked

LLMs & NLP

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

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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.

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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.

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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.

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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.

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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.

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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.

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Instruction Tuning

📘 Definition

Instruction tuning is the process of further training a pretrained language model on examples of instructions and appropriate responses so that it becomes better at following user requests.

🪄 Simple Explanation

Instruction tuning teaches a language model how to respond more effectively to human instructions.

🔑 Why It Matters

Pre-training teaches broad language patterns, while instruction tuning helps transform a general language model into an assistant that can respond usefully to diverse tasks.

💡 Example

A model may be trained on examples pairing instructions such as 'Summarise this passage' with high-quality summaries.

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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.

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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.

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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.

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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.

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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.

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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.

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Perplexity

📘 Definition

Perplexity is a metric used to evaluate how well a language model predicts a sequence of tokens. Lower perplexity generally indicates that the model assigns higher probability to the observed text.

🪄 Simple Explanation

Perplexity indicates how surprised a language model is by the text it is trying to predict.

🔑 Why It Matters

It is useful for comparing similar language models or training checkpoints, although it does not by itself measure factual accuracy, reasoning, safety, or usefulness.

💡 Example

When two similar language models are tested on the same corpus, the one with lower perplexity predicts the text more confidently on average.

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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.

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Reinforcement Learning from Human Feedback

📘 Definition

Reinforcement Learning from Human Feedback (RLHF) is a training approach that uses human preferences or preference models to help shape an AI system's behaviour towards responses people judge to be more useful or appropriate.

🪄 Simple Explanation

RLHF uses human preferences to help an AI model learn which kinds of responses people prefer.

🔑 Why It Matters

RLHF has been used to improve instruction following and align language-model behaviour more closely with human expectations, although it does not guarantee correctness or safety.

💡 Example

Human reviewers may compare two model responses, and those preference signals can be used to train the system towards more desirable behaviour.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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