A large language model is a type of artificial intelligence that uses a massive neural network to process and generate human-like text.
It learns patterns and relationships from extensive training data, allowing it to understand and generate human language with contextual coherence, creativity, and the ability to perform various language-related tasks. These models have millions or even billions of parameters, enabling them to handle a wide range of tasks such as text generation, translation, summarization, question answering, and more.
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A large language model is an advanced artificial intelligence system that employs a complex neural network with millions or billions of parameters to understand and generate human-like text. It can perform tasks such as text generation, translation, summarization, and more.
A large language model learns patterns and relationships from vast amounts of text data during training. It uses this knowledge to predict the likelihood of words and phrases, allowing it to generate coherent and contextually relevant text in response to input.
Examples include GPT-3 (Generative Pre-trained Transformer 3), BERT (Bidirectional Encoder Representations from Transformers), and T5 (Text-to-Text Transfer Transformer).
Large language models have a wide range of applications, including text generation, translation, summarization, sentiment analysis, chatbots, content creation, virtual assistants, coding assistance, and more.
Yes, large language models excel at understanding context. They analyze surrounding text to generate responses that maintain coherence and relevance within the given context.
Large language models are trained on massive datasets containing text from books, articles, websites, and other sources. They learn to predict the next word in a sequence based on the context of the preceding words.
While large language models can generate creative and diverse text, their creativity is based on patterns learned from training data. They can produce novel combinations of words, but their creativity is guided by the information they've been exposed to.
Large language models can generate text efficiently, but they lack true understanding, emotions, and critical thinking. While they can assist with content creation, human creativity, expertise, and nuanced thinking remain valuable.
Yes, large language models can inadvertently inherit biases present in their training data. Efforts are made to reduce bias, but vigilance is required to ensure fair and unbiased outputs.
Yes, ethical concerns include potential biases, misinformation propagation, deepfakes, and the responsible use of AI-generated content. Researchers and developers are actively addressing these concerns.
A large language model (LLM) is a computational model notable for its ability to achieve general-purpose language generation and other natural language processing tasks such as classification. Based on language models, LLMs acquire these abilities by learning statistical relationships from text documents during a computationally intensive self-supervised and semi-supervised training process. LLMs can be used for text generation, a form of generative AI, by taking an input text and repeatedly predicting the next token or word.
LLMs are artificial neural networks. The largest and most capable, as of March 2024, are built with a decoder-only transformer-based architecture while some recent implementations are based on other architectures, such as recurrent neural network variants and Mamba (a state space model).
Up to 2020, fine tuning was the only way a model could be adapted to be able to accomplish specific tasks. Larger sized models, such as GPT-3, however, can be prompt-engineered to achieve similar results. They are thought to acquire knowledge about syntax, semantics and "ontology" inherent in human language corpora, but also inaccuracies and biases present in the corpora.
Some notable LLMs are OpenAI's GPT series of models (e.g., GPT-3.5 and GPT-4, used in ChatGPT and Microsoft Copilot), Google's PaLM and Gemini (the latter of which is currently used in the chatbot of the same name), xAI's Grok, Meta's LLaMA family of models, Anthropic's Claude models, Mistral AI's models, and Databricks' DBRX.
See also: https://en.wikipedia.org/wiki/Large_language_model#List