Large Language Models (LLMs)

• Large Language Models (LLMs) are a type of artificial intelligence (AI) algorithm that uses deep learning and large datasets to understand, summarize, generate, and predict new content.
• Generative AI is closely related to LLMs, as they are a type of generative AI specifically designed to create text-based content.

Language in AI

• For thousands of years, humans have developed spoken languages for conversation.
• Language is fundamental to all forms of human and technological communication, containing the words, semantics, and grammar needed to convey ideas and concepts.
• In AI, a language model serves a similar purpose, forming the basis for communication and the creation of new concepts.

History of Language Models

• Early AI language models date back to the early days of AI development.
• Eliza, introduced in 1966 at MIT, is one of the earliest examples of an AI language model.
• All language models are initially trained on a dataset and then use various methods to identify relationships before generating new content based on the training.
• Language models are commonly used in Natural Language Processing (NLP) applications, where users input queries in natural language to obtain results.
• Natural Language Processing (NLP) is essential for fully and effectively processing text and speech data, overcoming differences in dialects, slang, and grammatical errors common in everyday conversations.

LLMs Explained

• LLMs represent an evolution of the language model concept in AI.
• LLMs significantly expand the data used for training and logical deduction, enhancing the AI model's capabilities.
• While there is no universally accepted standard for the size of a dataset required for training, LLMs typically have at least a billion or more parameters.
• Parameters are machine learning variables within the model that are used to generate new content after training.
• Modern LLMs emerged in 2017, employing transformer models, which are neural networks known as transformers.
• With many parameters and transformer models, LLMs can quickly understand and generate accurate responses, leading to the widespread use of AI technology across various industries.

Foundation Models

• LLMs are also called foundation models, a term introduced by Stanford’s Institute for Human-Centered AI in 2021.
• A foundation model is extensive and effective, serving as a basis for further optimization and specific use cases.
• Creating systems that deeply understand and apply human communication is a difficult task.
• This involves models that can process vast amounts of data, recognize linguistic patterns, and generate contextually appropriate responses akin to those of native speakers.
• The functionality of any LLM relies on several core components working harmoniously to achieve this level of linguistic understanding; each plays a crucial role in processing, learning, and generating language tailored to user needs.

Use Cases for LLMs

• LLMs can be used for text generation, and summarization.
• LLMs generate text in response to prompts or questions, functioning as generative AI.
• For instance, the publicly available LLM ChatGPT can create essays, poems, and other text forms in response to user input.
• Any large and complex dataset, including programming languages, can be used to train LLMs.
• Some LLMs can assist programmers in writing code and generate functions or complete programming source code.
• LLMs can also be applied in:
  • Sentiment analysis
  • DNA research
  • Customer service
  • Chatbots
  • Online searches
• Examples of LLMs include ChatGPT (OpenAI), Bard (Google), Llama (Meta), and Bing Chat (Microsoft).
• GitHub’s Copilot is another example, which assists in coding rather than natural human language.

Training LLMs

• The advancement of Large Language Models (LLMs) has led to a paradigm shift in the field of Natural Language Processing (NLP).
• Trained on vast amounts of text from the internet, LLMs can master new tasks using contextual learning mechanisms.
• NLP specialists do not update the models' parameters when ‘teaching’ them to solve specific problems.
• Instead, specialists write instructions or provide a few examples that demonstrate the desired behavior to the LLM.
• These demonstrations are given to the models as input context (referred to as "contextual learning"), and the models use the information from these examples to respond to similar prompts.