MTEB (Massive Text Embedding Benchmark)
MTEB, short for Massive Text Embedding Benchmark, is the standard public leaderboard for evaluating text embedding models across many task types at once.
Natural Language Processing
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Machine Translation
Machine translation (MT) is the automatic translation of text or speech from one human language into another.
Machine learning terms/Natural Language Processing
Natural Language Processing (NLP) is the subfield of artificial intelligence and machine learning concerned with enabling computers to read, interpret, generate, and reason about human language in text and…
Machine-generated text detection
Machine-generated text detection is the problem of deciding whether a given passage of text was written by a person or produced by a large language model.
Masked Language Model
A masked language model (MLM) is a language model trained to predict missing tokens that have been hidden in a sequence of text, using context from both the left and the right of each blank.
MathArena
MathArena is a public, continuously updated leaderboard and evaluation platform that measures the performance of large language models on mathematics competition problems released after each model's training…
Matryoshka representation learning
Matryoshka Representation Learning (MRL) is a representation learning technique that trains a single neural model to produce embedding vectors which remain useful when truncated to many smaller dimensionalities
MedQA
MedQA is a large-scale, open-domain medical question answering benchmark of multiple-choice questions taken from real medical licensing examinations, introduced by Di Jin and colleagues at MIT in 2020.
Meta Prompting
Meta prompting (also spelled meta-prompting) is an advanced prompt engineering technique where large language models (LLMs) are used to generate, refine, critique, select, or optimize prompts for themselves or…
Mike Lewis
Mike Lewis is a British natural language processing researcher based in Seattle who serves as a research scientist at Meta AI (Facebook AI Research, FAIR) and as the pre-training research lead on the Llama…
Multimodal Model
A multimodal model is a machine learning model, or a model-based system, that processes, relates, or produces information across more than one kind of data. Each kind is called a modality.
N-gram
An n-gram is a contiguous sequence of n items extracted from a given sample of text or speech, where the items can be characters, syllables, words, or other linguistic units.
NLU
Natural Language Understanding (NLU) is the subfield of artificial intelligence and natural language processing that turns unstructured human language into structured representations a computer can act on…
Named entity recognition
Named entity recognition (NER) is the natural language processing task of locating spans of text that name real-world things, such as people, organizations, and locations, and classifying each span into a…
Natural Language Processing
Natural language processing (NLP) is the study and development of computational methods for analyzing, representing, retrieving, transforming, and generating human language.
Natural Language Understanding
Natural language understanding (NLU) is the branch of artificial intelligence and computational linguistics that enables machines to read human language and extract its meaning, intent, and structure.
Natural language generation
Natural language generation (NLG) is the subfield of natural language processing and artificial intelligence concerned with building systems that produce understandable text in English or other human…
Natural language inference (NLI)
Natural language inference (NLI), also known as recognising textual entailment (RTE), is the natural language processing task of deciding whether a hypothesis sentence is entailed by, contradicts, or is…
Nemotron-CC
Nemotron-CC is a large-scale, open English-language pretraining dataset for large language models released by NVIDIA in December 2024.
Neural Machine Translation
Neural machine translation (NMT) is an approach to machine translation in which a single artificial neural network, trained end to end on bilingual text
Next-token prediction
Next-token prediction is the training objective used by most modern language models: the model reads a prefix of tokenized text, outputs a probability distribution over which token comes next, and training…
No Language Left Behind (NLLB)
No Language Left Behind (NLLB) is a machine translation research project and model family from Meta AI, announced in July 2022.
OCR Models
OCR Models are artificial intelligence (AI) systems that convert images of typed, handwritten, or printed text into machine-readable digital text through Optical Character Recognition (OCR).
OPUS-MT
OPUS-MT is a large collection of open, freely licensed neural machine translation models and tools produced by the Language Technology Research Group at the University of Helsinki.
PIQA
PIQA (Physical Interaction Question Answering) is a benchmark dataset of roughly 21,000 binary multiple-choice questions that evaluates the physical commonsense reasoning abilities of natural language…
PaLM
PaLM (Pathways Language Model) is a family of dense, decoder-only large language models developed by Google Research. Google announced the original family on April 4, 2022.
Perplexity
Perplexity has two distinct meanings in artificial intelligence. In information theory and natural language processing, perplexity (PPL) is an intrinsic score for a probabilistic model: on a specified sequence
Pleias
Pleias (stylized PleIAs) is a Paris based artificial intelligence laboratory and small company that designs, pretrains, and releases large language models trained exclusively on public domain and permissively…
Positional encoding
Positional encoding is a technique used to inject information about token order into transformer models.
Post-training
Post-training is the stage of large language model (LLM) development that comes after pre-training and turns a raw, general-purpose base model into an aligned, instruction-following AI assistant.
Pre-Trained Model
A pre-trained model is a machine learning model that has already been trained on a large, general-purpose dataset and can then be reused, either as a fixed feature extractor or by fine-tuning
Pre-training
Pre-training is a stage of machine learning in which a model learns parameters from a source dataset or source objective before those parameters are reused or adapted for a target use.
Prompt Engineering
Prompt engineering is the systematic design and testing of the inputs supplied to a generative model so that the model is more likely to produce a useful result.
PubMedQA
PubMedQA is a biomedical question answering dataset and benchmark that evaluates whether machine learning models can answer yes/no/maybe research questions using evidence from PubMed abstracts.
Question Answering Models
Question answering (QA) models are natural language processing systems that take a natural-language question as input and return a natural-language answer, optionally grounded in a supplied passage, document…
Question answering
Question answering (QA) is the task of producing an answer to a question, usually expressed in natural language.
QuillBot
QuillBot is an artificial intelligence-powered writing platform, founded in 2017, whose AI paraphrasing tool is its flagship product alongside a grammar checker, summarizer, plagiarism and AI-content detector…
Qwen
Qwen is a family of large language models and multimodal models developed by the Qwen Team at Alibaba Cloud.
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…
Reranker
A reranker (also called a cross-encoder reranker or rerank model) is a neural model used in retrieval augmented generation and information retrieval pipelines to re-score an initial set of candidate documents…
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…
SEDD (Score Entropy Discrete Diffusion)
Score Entropy Discrete Diffusion (SEDD) is a discrete diffusion model for language and other discrete data introduced by Aaron Lou, Chenlin Meng, and Stefano Ermon at Stanford University in the paper Discrete…
SPLADE
SPLADE (Sparse Lexical and Expansion model) is a learned sparse retrieval model that encodes a query or document as a weighted
SQuAD
SQuAD (the Stanford Question Answering Dataset) is a large-scale reading comprehension benchmark from Stanford University in which a model must answer a question by extracting the exact span of text that…
SciBERT
SciBERT is a BERT-based language model pretrained from scratch on a large corpus of scientific papers, built by the Allen Institute for AI (AI2).
SeamlessM4T
SeamlessM4T (short for Massively Multilingual and Multimodal Machine Translation) is a machine translation model released by Meta AI on August 22, 2023.
Semantic Scholar
Semantic Scholar is a free, AI-powered academic search engine and open research-data platform built by the Allen Institute for AI (AI2, also styled Ai2)
Semantic chunking
Semantic chunking is a family of document-segmentation strategies for retrieval augmented generation pipelines that places chunk boundaries based on similarity between sentence embeddings rather than fixed…
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.
Sentence Similarity Models
Sentence similarity models are machine learning systems that map natural language sentences to fixed-length numerical vectors (sentence embeddings) so that semantically related sentences sit close together in…
Sentence-BERT (SBERT)
Sentence-BERT (SBERT) is a modification of the pretrained BERT transformer network that produces semantically meaningful, fixed-size sentence embeddings comparable with simple cosine similarity.
Sentence-transformers/all-MiniLM-L6-v2 model
Sentence-transformers/all-MiniLM-L6-v2 model
Sentence-transformers/all-mpnet-base-v2 model
sentence-transformers/all-mpnet-base-v2
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.