What Are Large Language Models (LLM)

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Large Language models are powerful algorithms designed to understand and generate human-like text. They learn from vast amounts of text data and can perform tasks such as text completion, translation, summarization, and more. LLMs are a type of AI system that uses deep learning on massive text datasets to recognize patterns and generate remarkably fluent, contextual responses. These models are “unsupervised” – they learn in a general way without being explicitly trained for specific tasks.

LLMs do not store facts they store probabilities!

Mark Hennings / Entry Point AI: Prompt Engineering, RAG, and Fine-tuning: Benefits and When to Use (youtube.com)

LLMs are characterized by their ability to handle a vast number of parameters, with some of the most successful models having hundreds of billions of parameters. These models are trained using massive amounts of data and utilize self-supervised learning to predict the next token in a sentence, given the surrounding context.

Key Characteristics of LLMs:

  • Deep Learning and Data: LLMs leverage deep learning techniques and large datasets to understand, summarize, generate, and predict new content.
  • Transformer Architecture: LLMs utilize a transformer model architecture, consisting of an encoder and a decoder with self-attention capabilities, to extract meanings from a sequence of text and understand the relationships between words and phrases.
  • Training Process: LLMs undergo computationally intensive self-supervised and semi-supervised training processes to acquire their language generation and NLP capabilities.

LLMs have a broad range of applications, including language translation, sentence completion, sentiment analysis, question answering, and mathematical equations, among others. This allows LLMs to display versatile language abilities across many domains from creative writing to analysis to coding.

Large Language Models (LLMs) are powerful machine learning models that excel in natural language processing tasks, leveraging deep learning techniques, massive datasets, and self-supervised learning to understand and generate text-based content.

GPT-4 is a large language model (LLM), which is a neural network trained on massive amounts of data to understand and generate text.

The Rise of LLMs

Large Language Models (LLMs) have gained immense popularity due to their impressive capabilities. Models like

  • OpenAI (GPT-3, GPT-4)
  • Google (Gemini, LaMDA, PaLM)
  • Anthropic (Claude)
  • DeepMind (Gato / Gopher)
  • X AI (Grok)
  • Mistral (Mistral-2)
  • Nvidia/Microsoft ( MT-NLG)
  • Meta (OPT, LLaMA, LLaMA-2)
  • Standford Alpaca
  • Complete list at: Models – Hugging Face

LLMS have demonstrated remarkable proficiency in natural language understanding and generation. They can generate coherent paragraphs, answer questions, and even compose poetry. For example, GPT-4 is a multi-modal LLM that is capable of processing text and image input.

As LLM capabilities continue to evolve, we’ll likely see decoupled systems that combine the benefits of open-ended language models with specialized, controlled modules optimized for different applications.

LLMParameter Count
* Estimated
Claude-3 Opus2 trillion dollars*Anthropic
Wu Dao 2.01.75 trillionBeijing Academy of Artificial Intelligence, (BAAI)
Gemini 1.01.6 trillionGoogle
LaMDA1.56 trillionGoogle
Megatron-Turing NLG530 billionMicrosoft/Nvidia
MT-NLG530 billionGoogle
GPT-4340 billionOpenAI
Gopher280 billionDeepMind
GPT-3175 billionOpenAI
Bloom176 billionHugging Face/IDRI
Jurassic-1 Jumbo178 billionAI21 Labs
PanGu-Alpha200 billionHuawei/Beijing Academy of AI
LLaMa-2 70 billion Meta
GPT-NeoX-20B20 billionEleutherAI
GPT-J6 billionEleutherAI
Claude-110 billionAnthropic
AlphaFold100+ millionDeepMind

Full List at:

All Large Language Models Directory – All LLMs (llmmodels.org)

Other Resources:

Open Source Models – Hugging Face

The Large Language Model (LLM) Index | Sapling

GitHub – Barnacle-ai/awesome-llm-list: An overview of Large Language Model (LLM) options

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