LLM

Extracted Attributes

• Type

  Artificial Intelligence / Machine Learning Model

• Scale

  Billions to trillions of parameters

• Hardware Support

  Graphics Processing Units (GPUs) and Neural Processing Units (NPUs)

• Core Architecture

  Transformer

• Alignment Technique

  Reinforcement Learning from Human Feedback (RLHF)

• Primary Capabilities

  Text generation, summarization, translation, reasoning, and code generation

• Training Methodology

  Self-supervised learning

Timeline

• The transformer architecture is introduced, replacing recurrence with self-attention and enabling scalable training. (Source: Wikipedia)

  2017-01-01

• Emergence of models like GPT and BERT, demonstrating few-shot learning and compositional reasoning. (Source: Wikipedia)

  2018-01-01

• Satya Nadella discusses the commoditization of LLMs and the business revolution of AI agents at Davos. (Source: Document 4e50eb82-56c2-4d20-910f-9a43912c1cd7)

  2024-01-15

Large language model

A large language model (LLM) is a language model trained with self-supervised machine learning on a vast amount of text, designed for natural language processing tasks, especially language generation. The largest and most capable LLMs are generative pre-trained transformers (GPTs) and provide the core capabilities of modern chatbots. LLMs can be fine-tuned for specific tasks or guided by prompt engineering. These models acquire predictive power regarding syntax, semantics, and ontologies inherent in human language corpora, but they also inherit inaccuracies and biases present in the data they are trained on. They consist of billions to trillions of parameters and operate as general-purpose sequence models, generating, summarizing, translating, and reasoning over text. LLMs represent a significant new technology in their ability to generalize across tasks with minimal task-specific supervision, enabling capabilities like conversational agents, code generation, knowledge retrieval, and automated reasoning that previously required bespoke systems. LLMs evolved from earlier statistical and recurrent neural network approaches to language modeling. The transformer architecture, introduced in 2017, replaced recurrence with self-attention, allowing efficient parallelization, longer context handling, and scalable training on unprecedented data volumes. This innovation enabled models like GPT, BERT, and their successors, which demonstrated emergent behaviors at scale, such as few-shot learning and compositional reasoning. Reinforcement learning, particularly policy gradient algorithms, has been adapted to fine-tune LLMs for desired behaviors beyond raw next-token prediction. Reinforcement learning from human feedback (RLHF) applies these methods to optimize a policy, the LLM's output distribution, against reward signals derived from human or automated preference judgments. This has been critical for aligning model outputs with user expectations, improving factuality, reducing harmful responses, and enhancing task performance. Benchmark evaluations for LLMs have evolved from narrow linguistic assessments toward comprehensive, multi-task evaluations measuring reasoning, factual accuracy, alignment, and safety. Hill climbing, iteratively optimizing models against benchmarks, has emerged as a dominant strategy, producing rapid incremental performance gains but raising concerns of overfitting to benchmarks rather than achieving genuine generalization or robust capability improvements.

Web Search Results

• What is a large language model (LLM)?

  ## Question and Answer ## What is a large language model (LLM)? A large language model (LLM) is a type of artificial intelligence that can generate human language and perform related tasks. These models are trained on huge datasets, often containing billions of words. By analyzing all this data, the LLM learns patterns and rules of language, similar to the way a human learns to communicate through exposure to language. LLMs can perform various language tasks, such as answering questions, summarizing text, translating between languages, and writing content. Some examples of LLMs include ChatGPT, Claude, Microsoft Copilot, Gemini, and Meta AI. [...] Skip to Main Content Jump to navigation My account Ask Us 1. University of Arizona Libraries 2. University of Arizona Libraries # What is a large language model (LLM)? A large language model (LLM) is a type of artificial intelligence that can generate human language and perform related tasks. These models are trained on huge datasets, often containing billions of words. By analyzing all this data, the LLM learns patterns and rules of language, similar to the way a human learns to communicate through exposure to language. LLMs can perform various language tasks, such as answering questions, summarizing text, translating between languages, and writing content. Some examples of LLMs include ChatGPT, Claude, Microsoft Copilot, Gemini, and Meta AI.

• What is an LLM (large language model)? - Cloudflare

  ## What is a large language model (LLM)? A large language model (LLM) is a type of artificial intelligence (AI) program that can recognize and generate text, among other tasks. LLMs are trained on huge sets of data — hence the name "large." LLMs are built on machine learning: specifically, a type of neural network called a transformer model. [...] In simpler terms, an LLM is a computer program that has been fed enough examples to be able to recognize and interpret human language or other types of complex data. Many LLMs are trained on data that has been gathered from the Internet — thousands or millions of gigabytes' worth of text. Some LLMs continue to crawl the web for more content after they are initially trained. But the quality of the samples impacts how well LLMs will learn natural language, so an LLM's programmers may use a more curated data set, at least at first. [...] Sign upSales: +1 (888) 99 FLARE # What is a large language model (LLM)? Large language models (LLMs) are machine learning models that can comprehend and generate human language text. They work by analyzing massive data sets of language. #### Learning Objectives After reading this article you will be able to: Define large language model (LLM) Understand the applications for LLMs Explain how LLMs work Related Content What is artificial intelligence (AI)?What is generative AI?What is machine learning?Predictive AIWhat is agentic AI? #### Want to keep learning? Subscribe to theNET, Cloudflare's monthly recap of the Internet's most popular insights! Copy article link ## What is a large language model (LLM)?

• Large language model - Wikipedia

  Outline of machine learning v t e A large language model (LLM) is a language model trained with self-supervisedmachine learning on a vast amount of text, designed for natural language processing tasks, especially language generation.( The largest and most capable LLMs are generative pre-trained transformers (GPTs) and provide the core capabilities of modern chatbots. LLMs can be fine-tuned "Fine-tuning (deep learning)") for specific tasks or guided by prompt engineering.( These models acquire predictive power regarding syntax, semantics, and ontologies "Ontology (information science)")( inherent in human language corpora, but they also inherit inaccuracies and biases present in the data they are trained on.(

• What is LLM? - Large Language Models Explained - AWS

  ## What are Large Language Models? Large language models, also known as LLMs, are very large deep learning models that are pre-trained on vast amounts of data. The underlying transformer is a set of neural networks that consist of an encoder and a decoder with self-attention capabilities. The encoder and decoder extract meanings from a sequence of text and understand the relationships between words and phrases in it. Transformer LLMs are capable of unsupervised training, although a more precise explanation is that transformers perform self-learning. It is through this process that transformers learn to understand basic grammar, languages, and knowledge. [...] ## Why are large language models important? Large language models are incredibly flexible. One model can perform completely different tasks such as answering questions, summarizing documents, translating languages and completing sentences. LLMs have the potential to disrupt content creation and the way people use search engines and virtual assistants. While not perfect, LLMs are demonstrating a remarkable ability to make predictions based on a relatively small number of prompts or inputs. LLMs can be used for generative AI (artificial intelligence) to produce content based on input prompts in human language. LLMs are big, very big. They can consider billions of parameters and have many possible uses. Here are some examples:

• LLM (Large Language Models): What are they and examples?

  ## Other common uses and applications of LLMs Large Language Models (LLMs) have a wide range of applications in everyday life and professional settings. Some of the standouts are: [...] Information analysis: they interpret large volumes of textual data, detect patterns, classify texts, and even analyze user sentiment (positive, negative, or neutral). Response to knowledge bases: they can search and give concrete answers in files, manuals, or corporate documentation, streamlining internal processes. Generation and support in programming: they help to write code in different languages, detect errors, optimize SQL queries, or even create web pages from a natural language instruction. Classification and organization of texts: they group similar documents or comments, something useful in research, market analysis, or brand reputation management. [...] Share ## What are LLMs (Large Language Models)? LLMs (Large Language Models) are Artificial Intelligence (AI) systems designed to understand and generate human language using deep learning models. They use large volumes of data and advanced algorithms to provide solutions and create relevant content in a variety of applications, thanks to their natural language processing (NLP) and machine learning capabilities. ## What are LLMs for? LLMs can transform industries by improving efficiency, productivity, and creativity. Some of their most noteworthy applications are: Customer service Large Language Models provide quick and precise answers to queries from users. They solve technical problems, guide through complex procedures, and offer personalized assistance 24 hours a day.

• Instance Of

  Q4167410