core concepts

Large Language Model

A neural network trained on massive text datasets to understand and generate human language. LLMs use billions of parameters to capture statistical patterns in language, enabling them to perform tasks like translation, summarization, and code generation.

In practice

GPT-4, Claude, and Gemini are all LLMs capable of holding complex conversations.

Transformer
A neural network architecture introduced in the 2017 paper 'Attention Is All You Need' that processes sequential data using self-attention mechanisms instead of recurrence. Transformers enable massive parallelization during training, making them the foundation of modern LLMs and vision models.
GPT
Generative Pre-trained Transformer, a family of autoregressive language models developed by OpenAI. GPT models are pre-trained on large corpora of text using unsupervised learning, then fine-tuned for specific tasks. The architecture uses decoder-only transformers.
Token
The basic unit of text that language models process. Tokens can be words, parts of words, or individual characters depending on the tokenizer used. Most models process text by converting it into a sequence of token IDs.
Foundation Model
A large-scale AI model trained on broad data that can be adapted to a wide range of downstream tasks. Foundation models serve as a base that can be fine-tuned or prompted for specific applications without training from scratch.

In the index

Tools that mention Large Language Model

Bloomberg GPTTrust 88

Matched on each tool’s own description and feature list, highest trust score first.

Briefings

Large Language Model

Tools that mention Large Language Model

Further reading