Sequence-to-Sequence Task
A sequence-to-sequence (seq2seq) task is any machine learning problem in which a model receives a variable-length input sequence and produces a variable-length output sequence, where the input and output may…
Natural Language Processing
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Similarity Measure
A similarity measure (also called a similarity function or similarity metric) is a real-valued function that quantifies the degree of resemblance between two objects, returning a large value when the objects…
SimpleBench
SimpleBench is a text-only benchmark for large language models created by Philip, the host of the AI Explained YouTube channel, with collaborator Hemang.
SimpleQA
SimpleQA is a factuality benchmark released by OpenAI on October 30, 2024 that measures whether large language models can answer short, fact-seeking questions correctly instead of producing hallucinations.
Speech Recognition
Speech recognition, usually called automatic speech recognition (ASR), is the computational task of converting a spoken-language signal into a sequence of written symbols.
Structured output
Structured output is a set of techniques and API features that constrain a large language model (LLM) to emit responses that exactly conform to a predefined format or schema, such as JSON, XML, or a custom…
Summarization Models
Summarization models are natural language processing systems that condense a source document, set of documents, or dialogue into a shorter version that preserves the most important information.
SuperGLUE
SuperGLUE is a public benchmark for evaluating general-purpose English language understanding in machine learning systems, consisting of eight challenging reasoning and reading-comprehension tasks, two…
Supervised fine-tuning
Supervised fine-tuning (SFT) is supervised training applied to a model that has already been trained.
TF-IDF (Term Frequency-Inverse Document Frequency)
TF-IDF (term frequency-inverse document frequency) is a numerical statistic that measures how important a word is to a single document within a larger collection or corpus, computed as the product of two…
Table Question Answering Models
Table question answering models (TableQA models) are machine learning systems that answer natural language questions over structured tabular data such as spreadsheets, database tables, and HTML tables…
Temperature (artificial intelligence)
Temperature is a hyperparameter that controls the randomness of a large language model's output by scaling the model's raw scores, called logits
Text Classification Models
Text classification models are machine learning systems that assign one or more predefined categorical labels to a span of natural language text, such as positive vs. negative, spam vs. ham
Text Generation Models
Text generation models are language models trained to produce coherent natural-language text by predicting tokens one at a time, each conditioned on the preceding context.
Text summarization
Text summarization is the natural language processing (NLP) task of automatically producing a shorter version of one or more documents that preserves the most important information from the original text.
Text-to-Speech
Text-to-speech (TTS) refers to artificial intelligence systems that convert written text into natural-sounding spoken audio.
Text2Text Generation Models
Text-to-text (text2text) generation models are a family of neural network systems that frame many natural language processing tasks as a single problem: given an input text string
The Pile (dataset)
The Pile is an 825.18 GiB (approximately 886 GB) English text corpus designed for training large language models, assembled from 22 diverse, high-quality subsets spanning academic, professional, internet…
Token
A token is the basic unit of text that a language model reads and writes: a word, a subword fragment, a single character, or a byte, produced by splitting text during a step called tokenization.
Token Classification Models
Token classification models are natural language processing systems that assign a discrete label to every token in an input sequence, where a token is typically a word, subword piece, or character.
Tokenization
Tokenization is the process of breaking text into smaller units called tokens, which serve as the fundamental input to natural language processing (NLP) systems and large language models (LLMs).
Tool use
Tool use in artificial intelligence is the ability of a model-based system to request, coordinate, and use capabilities outside the model's ordinary token-generation process.
Top-k sampling
Top-k sampling is a decoding strategy for autoregressive language models that restricts each generation step to the k most probable next tokens.
Top-p and top-k sampling
Top-p sampling, also called nucleus sampling, is a text-generation decoding method for large language models (LLMs) that, at each step, samples the next token from the smallest set of highest-probability…
Top-p sampling
Top-p sampling, also called nucleus sampling, is a stochastic decoding method for text generation in which the model samples from the smallest possible set of tokens whose cumulative probability mass exceeds a…
Topic model
A topic model is a statistical model that discovers the abstract "topics" hidden in a collection of documents, where each document is represented as a mixture of a small number of latent topics and each topic…
ToxiGen
ToxiGen is a large-scale, machine-generated dataset designed for adversarial and implicit hate speech detection.
Translation Models
Translation models are computational systems that convert text or speech from a source language into a target language.
Trigram
A trigram is a contiguous sequence of three items (most often three words) drawn from a sample of text or speech
TriviaQA
TriviaQA is a large-scale reading comprehension and question answering dataset of over 650,000 question-answer-evidence triples, introduced in 2017 by Mandar Joshi, Eunsol Choi, Daniel S. Weld
TruthfulQA
TruthfulQA is a benchmark designed to measure whether large language models (LLMs) generate truthful answers to questions.
Unidirectional language model
A unidirectional language model is a language model that predicts each token using only the tokens that come before it in the sequence (the left context), via causal (autoregressive) masking
Universal Speech Model
The Universal Speech Model (USM) is a family of large multilingual speech models developed by Google Research that performs automatic speech recognition (ASR) and speech-to-text translation across more than…
Vector embeddings
Vector embeddings are dense numerical representations of objects (text, images, audio, video, code, graphs, or any structured data) that map them into a continuous vector space such that semantic similarity…
Voyage AI
Voyage AI is an artificial intelligence company that builds state-of-the-art embedding and reranking models for retrieval-augmented generation and semantic search.
Voyage-3
Voyage-3 is a family of general-purpose text embedding models developed by Voyage AI, launched in September 2024 with voyage-3 and voyage-3-lite , expanded in January 2025 with voyage-3-large , and refreshed…
Wav2Vec
Wav2Vec is a family of self-supervised learning models from Meta AI (formerly Facebook AI Research) that learn speech representations directly from raw audio waveforms
Whisper
Whisper is an open-source family of automatic speech recognition (ASR) models developed by OpenAI and first released on September 21, 2022.
WinoGrande
WinoGrande is a large-scale benchmark for commonsense reasoning consisting of 44,000 binary fill-in-the-blank pronoun resolution problems, built to test whether language models genuinely understand commonsense…
Winograd Schema Challenge
The Winograd Schema Challenge (WSC) is a commonsense reasoning test in which a system must resolve an ambiguous pronoun in a short sentence where the correct answer flips when one or two words change
Word Embedding
A word embedding is a learned representation of text in which words are mapped to dense vectors of real numbers in a continuous vector space, so that words with similar meanings are positioned close together.
Word error rate
Word error rate (WER) is the standard metric for measuring the accuracy of an automatic speech recognition (ASR) system
WordNet
WordNet is a large lexical database of English developed at Princeton University beginning in 1985.
WordPiece
WordPiece is a subword tokenization algorithm that builds a fixed-size vocabulary of word pieces by repeatedly merging the symbol pair whose combination most increases the likelihood of the training corpus…
Wordtune
Wordtune is an artificial intelligence-powered writing and reading assistant developed by AI21 Labs, an Israeli AI company.
WritingBench
WritingBench is a comprehensive benchmark for evaluating the generative writing capabilities of large language models (LLMs) across diverse real-world writing tasks.
XLM-RoBERTa
XLM-RoBERTa (often abbreviated XLM-R) is a multilingual masked language model developed by Facebook AI Research (now Meta AI) and released in November 2019.
XLNet
XLNet is a generalized autoregressive pretraining method for natural language processing that combines the strengths of autoregressive and autoencoding language models.
Z.ai
Z.ai is the international brand of the Chinese artificial intelligence company Zhipu AI (智谱AI), a 2019 spinout from Tsinghua University that builds the open-weight General Language Model (GLM) family and…
ZebraLogic
ZebraLogic is a benchmark for evaluating the logical reasoning capabilities of large language models (LLMs).
Zero-Shot Classification Models
Zero-shot classification models are machine learning systems that assign input text to a set of candidate categories without having seen labeled training examples for those specific categories.
chrF
chrF is a machine translation evaluation metric that scores a candidate translation by counting the character n-grams it shares with one or more reference translations, then combining character n-gram…
fastText
fastText is an open-source library for learning word embeddings and performing text classification, developed by Facebook AI Research (FAIR) and released to the public on August 18, 2016 .
mT5
mT5 (multilingual T5) is a transformer-based encoder-decoder language model released by Google Research in October 2020 that covers 101 languages in a single model, pre-trained on a Common Crawl corpus called…
olmOCR
olmOCR is an open toolkit and vision-language model from the Allen Institute for AI (Ai2) that turns PDFs and document images into clean, structured plain text and Markdown.
text-embedding-3
text-embedding-3 is a family of third-generation text embedding models that OpenAI released on January 25, 2024.
tiktoken
tiktoken is an open source byte pair encoding (BPE) tokenizer library released by OpenAI in December 2022 that converts text into the integer token sequences its language models read and write.
word2vec
word2vec is a family of shallow neural network models, published by Tomas Mikolov and colleagues at Google in 2013
Łukasz Kaiser
Łukasz Kaiser is a Polish computer scientist and researcher at OpenAI who is one of the eight co-authors of the 2017 paper "Attention Is All You Need," the work that introduced the Transformer architecture…