AI21 Labs
AI21 Labs is an Israeli artificial intelligence company, founded in 2017 by Yoav Shoham, Ori Goshen, and Amnon Shashua, that develops large language models (LLMs) and AI orchestration systems for enterprise…
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Showing 1-58 of 58 articles
Agentic Context Engineering
Agentic Context Engineering (ACE) is a framework for scalable and efficient context adaptation in large language models (LLMs) that lets an AI system improve itself by treating its own context as an evolving
AlpacaEval
AlpacaEval is an automatic evaluation framework for instruction-following large language models (LLMs) developed by Stanford University's Tatsu Lab
Bert-base-uncased model
PyTorch, TensorFlow, JAX, Rust, Core ML, Safetensors, Transformers
Bidirectional language model
A bidirectional language model is a language model that, when computing a representation for a token, conditions on both the tokens that come before it (the left context) and the tokens that come after it (the…
Byte-Pair Encoding
Byte-pair encoding (BPE) is a subword tokenization algorithm that splits text into tokens by starting from individual characters or bytes and iteratively merging the most frequent adjacent pair into a new token
CamemBERT
CamemBERT is a French monolingual language model based on the RoBERTa architecture, released in late 2019 by researchers at Inria, Facebook AI Research, and Sorbonne Université.
Claude (language model)
Claude is a family of large language models (LLMs) developed by Anthropic, an American artificial intelligence safety and research company.
Claude Sonnet 4.5
Claude Sonnet 4.5 is a multimodal large language model (LLM) developed by Anthropic and released on September 29, 2025, which Anthropic described at launch as "the best coding model in the world." It is a…
Cohere
Cohere is a Canadian artificial intelligence company that develops language, retrieval, speech, and multimodal models for businesses and public-sector organizations.
Command R
Command R is a family of enterprise large language models from Cohere, launched in March 2024 and built specifically for retrieval-augmented generation (RAG), multi-step tool use, and grounded text generation…
Compass (Shopee language model)
Compass is a proprietary family of large language models developed by Shopee and its parent company, Sea, for Southeast Asian languages and e-commerce tasks.
Context window
A context window is the finite token sequence that a language model can process for one invocation.
DSPy
DSPy (short for Declarative Self-improving Python) is an open-source framework, developed at Stanford NLP, for programming rather than prompting large language models (LLMs).
Decoding strategies
Decoding strategies are the algorithms that select output tokens from a language model's next-token probability distribution during text generation.
Falcon (language model)
Falcon is a family of open-source large language models built by the Technology Innovation Institute (TII)
GPT-1
GPT-1 is the first model in the GPT (Generative Pre-trained Transformer) series, a 117-million-parameter, 12-layer decoder-only Transformer released by OpenAI on June 11, 2018 in the paper "Improving Language…
GPT-2
GPT-2 is a family of autoregressive language models introduced by OpenAI on February 14, 2019.
GPT-3
GPT-3 (Generative Pre-trained Transformer 3) is a family of decoder-only, autoregressive large language models developed by OpenAI.
GPT-4
GPT-4 (Generative Pre-trained Transformer 4) is a large language model developed by OpenAI and released on March 14, 2023.
Gemini (language model)
Gemini is a family of natively multimodal large language models developed by Google DeepMind, first announced on December 6, 2023, that can reason across text, images, audio, video, and code within a single…
Greedy decoding
Greedy decoding (also called greedy search or argmax decoding) is the simplest text-generation strategy used by autoregressive language models: at every step it picks the single highest-probability next token…
Grounding (artificial intelligence)
Grounding in artificial intelligence is the process of anchoring an AI system's outputs to verifiable
How to Pressure LLMs for Better Output
Pressuring large language models (LLMs) is a family of prompt engineering techniques that try to push a model toward better output by adding emotional weight, urgency, stakes, or coercion to the prompt.
IFEval
IFEval (Instruction-Following Evaluation) is a benchmark of 541 prompts that measures how reliably large language models obey explicit, machine-checkable instructions such as "write in more than 400 words,"…
In-Context Learning
In-context learning (ICL) is the ability of a large language model to learn a new task at inference time by conditioning on a prompt that contains a few input-output examples (demonstrations)
InfiniteBench
InfiniteBench (stylized as ∞Bench) is a long-context benchmark that tests whether large language models (LLMs) can genuinely process and reason over inputs longer than 100,000 tokens, using 12 tasks that span…
Instruction Tuning
Instruction tuning is the post-pretraining training stage in which a large language model (LLM) is fine-tuned on a curated collection of (instruction
Knowledge Editing
Knowledge editing (also called model editing) is a family of techniques for updating or correcting specific factual associations stored in the weights of a trained large language model without full retraining…
LLaMA
LLaMA is the original name of a family of large language models developed by Meta AI.
Large Language Model
A large language model (LLM) is an artificial intelligence system built on a transformer neural network with billions to trillions of parameters, trained on massive text corpora to predict the next token in a…
LegalBench
LegalBench is a collaboratively constructed benchmark for measuring legal reasoning in large language models (LLMs)
Llama 3
Llama 3 is a family of open-weight large language models developed by Meta. Meta released the original Llama 3 checkpoints on April 18, 2024, in 8-billion-parameter and 70-billion-parameter sizes.
LongBench
LongBench is a benchmark suite for evaluating the long-context understanding capabilities of large language models (LLMs).
Longformer
Longformer is a transformer architecture for processing long documents, introduced by Iz Beltagy, Matthew E. Peters
MT-Bench
MT-Bench (Multi-Turn Benchmark) is a benchmark of 80 hand-written, two-turn questions that evaluates large language models (LLMs) on multi-turn conversation and instruction following by using a strong model…
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…
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…
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.
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.
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.
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.
Qwen
Qwen is a family of large language models and multimodal models developed by the Qwen Team at Alibaba Cloud.
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.
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).
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…
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
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…
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
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…
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.
ZebraLogic
ZebraLogic is a benchmark for evaluating the logical reasoning capabilities of large language models (LLMs).
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…