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For example, a common word like "marketing" may be represented by a single token, while a less common word or technical phrase may be split into multiple tokens. You don’t need a technical background to understand the basics of how LLMs work, but there are several key concepts you should familiarize yourself with. This guide explains what LLMs are, how they work, and what business leaders must understand to use them effectively. It uses deep learning techniques, specifically neural networks with billions of parameters, to predict and produce coherent text, answer questions, translate languages, write code, and perform various other language-based tasks. A Large Language Model is an AI system trained on massive amounts of text data to understand and generate human-like language. The immediate success of these LLMs demonstrates a keen interest in robotic-type LLMs that emulate and, in some contexts, outperform the human brain.
Moving beyond n-gram models, researchers started in 2000 to use neural networks as language models. In the early 1990s, IBM's statistical models pioneered word alignment techniques for machine translation, laying the groundwork for corpus-based language modeling. Generative pre-trained transformers (GPTs) are a type of LLM that is pre-trained to predict the next word. LLMs can typically generate, summarize, translate, and analyze text thunder empire slot in many contexts. A large language model (LLM) is an AI model (typically a neural network) trained on a vast amount of text for natural language processing tasks, especially language generation.
According to a study by Luccioni, Jernite and Strubell (2024), simple classification tasks performed by AI models consume on average 0.002 to 0.007 Wh per prompt (about 9% of a smartphone charge for 1,000 prompts). The energy demands of LLMs have grown along with their size and capabilities. A 2023 study showed that when ChatGPT 3.5 turbo was prompted to repeat the same word indefinitely, after a few hundreds of repetitions, it would start outputting excerpts from its training data. Evaluations of controlled LLM output measure the amount memorized from training data (focused on GPT-2-series models) as variously over 1% for exact duplicates or up to about 7%. Memorization is an emergent behavior in LLMs in which long strings of text are occasionally output verbatim from training data, contrary to the typical behavior of traditional artificial neural networks. This may result in some of the model's safeguards being overcome (jailbreaking), a problem called prompt injection.
Around the same time, encoder–decoder models like Google’s T5 and Facebook’s BART showcased the strengths of the full sequence-to-sequence design for tasks such as translation and summarization. A major shift came in the 2010s with the rise of neural networks, with word embeddings like Word2Vec and GloVe, which represented words as vectors in continuous space, enabling models to learn semantic relationships. Speed, energy consumption, token throughput, memory footprint and ability to handle long context windows are some of the common metrics used to evaluate how efficiently LLMs are able to arrive at outputs. Fairness and bias evaluations can help practitioners prevent LLMs from reproducing harmful stereotypes or misinformation.
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