Question answering
Question answering (QA) is the task of producing an answer to a question, usually expressed in natural language.
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Showing 61-92 of 92 articles
ROUGE
ROUGE (Recall-Oriented Understudy for Gisting Evaluation) is a set of automatic metrics that score the quality of a machine-generated text summary by counting how many overlapping units (n-grams, word…
RedPajama
RedPajama is a family of large-scale, openly licensed datasets for training large language models (LLMs), created by Together AI with academic and open-source partners to reproduce, in fully open form
Reinforcement Learning from Human Feedback (RLHF)
Reinforcement learning from human feedback (RLHF) is a family of methods in which judgments supplied by people are converted into a reward signal and a policy is optimized with reinforcement learning against…
Reporting Bias
Reporting bias is a type of data bias in machine learning that occurs when the frequency of events, properties, or outcomes captured in a dataset does not reflect their real-world frequency, because people…
Retrieval-Augmented Generation
Retrieval-augmented generation (RAG) is a family of methods that retrieves information from an external collection and conditions a generative model on that information when producing an output.
RoBERTa
RoBERTa (Robustly Optimized BERT Pretraining Approach) is an open-source natural language processing model released in July 2019 by researchers at Facebook AI (now Meta AI) and the University of Washington…
Semantic search
Semantic search is an information retrieval approach that finds results based on the meaning and intent behind a query rather than relying solely on exact keyword matches.
SentencePiece
SentencePiece is an open-source, language-agnostic subword tokenization library and detokenizer developed at Google and introduced by Taku Kudo and John Richardson in their 2018 EMNLP system demonstration…
Sentiment Analysis
Sentiment analysis, also called opinion mining or emotion AI, is the use of natural language processing to automatically determine the sentiment, emotion, or opinion expressed in a piece of text, typically…
Sequence Model
A sequence model is a class of machine learning models that processes, generates, or predicts ordered data where the position and surrounding context of each element carry meaning.
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…
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…
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.
Supervised fine-tuning
Supervised fine-tuning (SFT) is supervised training applied to a model that has already been trained.
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 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.
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.
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).
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…
Trigram
A trigram is a contiguous sequence of three items (most often three words) drawn from a sample of text or speech
TruthfulQA
TruthfulQA is a benchmark designed to measure whether large language models (LLMs) generate truthful answers to questions.
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
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…
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 .
word2vec
word2vec is a family of shallow neural network models, published by Tomas Mikolov and colleagues at Google in 2013