Nlp Glossary

Browse 64 nlp terms defined in plain English, from the cultural dictionary of computing.

64 Nlp Terms

Attention
The core mechanism of transformer models that allows each token to dynamically attend to (weigh the importance of) every other token in the sequence....
Beam Search
A decoding strategy that keeps the top-scoring partial sequences instead of only the single best candidate. It influences how models are trained, evaluated, or...
BERT
A bidirectional transformer language model pre-trained with masked language modeling and next sentence objectives. It influences how models are trained,...
Byte Pair Encoding
A tokenization algorithm that repeatedly merges common symbol pairs to build a useful subword vocabulary. It influences how models are trained, evaluated, or...
Causal Language Model
A language model trained to predict the next token using only preceding context. It influences how models are trained, evaluated, or served, and it can...
Chatbot
A program that simulates conversation with human users. Ranges from ELIZA's 1966 pattern-matching parlor tricks to modern LLM-powered assistants that can debug...
ELMo
Embeddings from Language Models, a contextual word representation model built from bidirectional language models. It influences how models are trained,...
Embedding Model
A neural network trained to convert text, images, or other data into fixed-dimensional vectors (embeddings) that capture semantic meaning. Similar items...
GLUE Benchmark
A benchmark suite of language understanding tasks used to compare NLP model performance. It influences how models are trained, evaluated, or served, and it can...
Large Language Model
A neural network with billions of parameters trained on massive text datasets to understand and generate human language. GPT, Claude, Llama, and Gemini are...
LLM
Large Language Model — a neural network trained on vast amounts of text data that can generate, summarize, translate, and reason about human language.
LLM Agent
LLM Agent is an AI or ML concept used to represent, train, evaluate, or deploy learned systems. It is commonly used for building production models and research...
LLM Evaluation
LLM Evaluation is an evaluation concept used to measure model quality, robustness, or efficiency. It is commonly used for comparing systems before release or...
LLM Fine-Tuning
LLM Fine-Tuning is a stage of model optimization where weights or behaviors are adjusted from data or feedback. It is commonly used for foundation-model...
LLM Inference
LLM Inference is the phase where a trained model processes new inputs to produce predictions or generations. It is commonly used for production APIs, batch...
LLM Routing
LLM Routing is an AI or ML concept used to represent, train, evaluate, or deploy learned systems. It is commonly used for building production models and...
LLM Safety
LLM Safety is a safety or governance control for limiting harmful, noncompliant, or insecure model behavior. It is commonly used for production AI systems that...
Local LLM
Local LLM is an AI or ML concept used to represent, train, evaluate, or deploy learned systems. It is commonly used for building production models and research...
Long Context
Long Context is an AI or ML concept used to represent, train, evaluate, or deploy learned systems. It is commonly used for building production models and...
Masked Language Model
Masked Language Model is a model component or design choice that shapes how information flows through a learned system. It is commonly used for building neural...
Multimodal Embedding
Multimodal Embedding is a dense numerical representation that places semantically related items near each other in vector space. It is commonly used for...
Multimodal Tokenizer
Multimodal Tokenizer is a unit or control marker used when text is segmented and generated by language models. It is commonly used for prompt assembly,...
Named Entity Recognition
Named Entity Recognition is an application task where a model extracts structured meaning or predictions from raw input. It is commonly used for NLP and vision...
Natural Language Generation
Natural Language Generation is a generative modeling concept for producing new content such as text, images, audio, or video. It is commonly used for creative...
Natural Language Inference
Natural Language Inference is the phase where a trained model processes new inputs to produce predictions or generations. It is commonly used for production...
Natural Language Processing
Natural Language Processing is an evaluation concept used to measure model quality, robustness, or efficiency. It is commonly used for comparing systems before...
Natural Language Understanding
Natural Language Understanding is an application task where a model extracts structured meaning or predictions from raw input. It is commonly used for NLP and...
Neural Machine Translation
Neural Machine Translation is an application task where a model extracts structured meaning or predictions from raw input. It is commonly used for NLP and...
N-Gram
N-Gram is an AI or ML concept used to represent, train, evaluate, or deploy learned systems. It is commonly used for building production models and research...
OpenAI
OpenAI is an AI company and research lab that develops foundation models, developer APIs, and products such as ChatGPT. It is commonly used for building...
Output Token
Output Token is a unit or control marker used when text is segmented and generated by language models. It is commonly used for prompt assembly, decoding...
Perplexity
A metric that measures how well a probability model predicts a sample. Lower perplexity means the model is less 'surprised' by the data. Informally, it...
Positional Embedding
Positional Embedding is a dense numerical representation that places semantically related items near each other in vector space. It is commonly used for...
Prompt Caching
Prompt Caching is a prompting technique or prompt-side control used to shape large-model behavior at inference time. It is commonly used for chat systems,...
Prompt Chaining
Prompt Chaining is a prompting technique or prompt-side control used to shape large-model behavior at inference time. It is commonly used for chat systems,...
Prompt Engineering Detail
Prompt Engineering Detail is a prompting technique or prompt-side control used to shape large-model behavior at inference time. It is commonly used for chat...
Prompt Injection Detail
Prompt Injection Detail is a prompting technique or prompt-side control used to shape large-model behavior at inference time. It is commonly used for chat...
Prompt Template
Prompt Template is a prompting technique or prompt-side control used to shape large-model behavior at inference time. It is commonly used for chat systems,...
Prompt Tuning
Prompt Tuning is a prompting technique or prompt-side control used to shape large-model behavior at inference time. It is commonly used for chat systems,...
Rope Embedding
Rope Embedding is a dense numerical representation that places semantically related items near each other in vector space. It is commonly used for retrieval,...
Rotary Position Embedding
Rotary Position Embedding is a dense numerical representation that places semantically related items near each other in vector space. It is commonly used for...
Semantic Search
Search based on the meaning of queries rather than keyword matching. Converts both queries and documents into vector embeddings and finds the nearest matches...
Sentence Embedding
Sentence Embedding is a dense numerical representation that places semantically related items near each other in vector space. It is commonly used for...
Sentence Transformer
Sentence Transformer is an AI or ML concept used to represent, train, evaluate, or deploy learned systems. It is commonly used for building production models...
Sentiment Analysis
Sentiment Analysis is an application task where a model extracts structured meaning or predictions from raw input. It is commonly used for NLP and vision...
Seq2Seq
Seq2Seq is an AI or ML concept used to represent, train, evaluate, or deploy learned systems. It is commonly used for building production models and research...
Sequence Modeling
Sequence Modeling is a model component or design choice that shapes how information flows through a learned system. It is commonly used for building neural...
Sequence-to-Sequence
Sequence-to-Sequence is an AI or ML concept used to represent, train, evaluate, or deploy learned systems. It is commonly used for building production models...
Slot Filling
A task where a system extracts specific fields or arguments from text, such as dates, names, or booking details.
Stop Token
Stop Token is a unit or control marker used when text is segmented and generated by language models. It is commonly used for prompt assembly, decoding control,...
Subword Tokenization
Subword Tokenization is a unit or control marker used when text is segmented and generated by language models. It is commonly used for prompt assembly,...
System Prompt Detail
System Prompt Detail is a prompting technique or prompt-side control used to shape large-model behavior at inference time. It is commonly used for chat...
Text Embedding
Text Embedding is a dense numerical representation that places semantically related items near each other in vector space. It is commonly used for retrieval,...
Token
The basic unit of text that a language model processes — roughly 3/4 of an English word on average. Text is split into tokens by a tokenizer before being fed...
Token
The basic unit of text processed by a language model — roughly ¾ of a word on average. 'Unbelievable' might be two tokens: 'un' and 'believable.' Token limits...
Token Budget
Token Budget is a unit or control marker used when text is segmented and generated by language models. It is commonly used for prompt assembly, decoding...
Tokenization Detail
Tokenization Detail is a unit or control marker used when text is segmented and generated by language models. It is commonly used for prompt assembly, decoding...
Tokenizer
The component that splits text into tokens before feeding it to a language model. Different tokenizers produce different tokens — 'unhappiness' might become...
Token Limit
Token Limit is a unit or control marker used when text is segmented and generated by language models. It is commonly used for prompt assembly, decoding...
Tokens Per Second Detail
Tokens Per Second Detail is a unit or control marker used when text is segmented and generated by language models. It is commonly used for prompt assembly,...
Vector Embedding
Vector Embedding is a dense numerical representation that places semantically related items near each other in vector space. It is commonly used for retrieval,...
Word2Vec
Word2Vec is an AI or ML concept used to represent, train, evaluate, or deploy learned systems. It is commonly used for building production models and research...
Word Embedding
Word Embedding is a dense numerical representation that places semantically related items near each other in vector space. It is commonly used for retrieval,...
Zero-Shot Prompting
Zero-Shot Prompting is a prompting technique or prompt-side control used to shape large-model behavior at inference time. It is commonly used for chat systems,...

Related Topics