{"id":19705,"date":"2024-02-21T12:00:37","date_gmt":"2024-02-21T17:00:37","guid":{"rendered":"https:\/\/enterprise-knowledge.com\/?p=19705"},"modified":"2024-02-27T10:17:29","modified_gmt":"2024-02-27T15:17:29","slug":"what-is-a-large-language-model-llm","status":"publish","type":"post","link":"https:\/\/enterprise-knowledge.com\/what-is-a-large-language-model-llm\/","title":{"rendered":"What is a Large Language Model (LLM)?"},"content":{"rendered":"
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Note: The above image was generated using Dall-E 3 (via ChatGPT).<\/em><\/figcaption><\/figure>\n

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In late November of 2022, artificial intelligence (AI) research and development company OpenAI released ChatGPT, an AI chatbot powered by a Large Language Model (LLM). In the following year, the world witnessed a meteoric rise in the usage of ChatGPT and other LLMs across a diverse array of industries and applications. However, what large language models actually are and what they are capable of is often misunderstood. In this blog, I will define LLMs, explore how they work, explain their strengths and weaknesses, and elaborate on a few of the most common LLM use cases for the enterprise.<\/span><\/p>\n

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So, what is a Large Language Model?<\/b><\/h2>\n

In short, a Large Language Model is an advanced AI model designed to perform Natural Language Processing (NLP) tasks, including interpreting, translating, predicting, and generating coherent, contextually relevant text. LLMs require extensive training on vast textual datasets that contain trillions of words, like Wikipedia and GitHub, which teaches the model to recognize patterns in text. An LLM such as OpenAI\u2019s GPT-4 <\/span>isn\u2019t doing any \u201creasoning\u201d<\/span><\/a> like a human does, at least not yet \u2013 it is merely generating output that fits the patterns it has learned through training. It can simply be thought of as doing very sophisticated predictions of which words in which context go in what order.\u00a0<\/span><\/p>\n

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How does a Large Language Model work?\u00a0<\/b><\/h2>\n

All LLMs operate by leveraging immense, layered networks of interconnected nodes that process and transmit information. The structure of the networks draws inspiration from the interconnectedness of the human brain’s network of neurons. Within this framework, LLMs use so-called transformer models \u2013 consisting of an encoder and a decoder \u2013 to turn input into output.\u00a0<\/span><\/p>\n

In the process of handling a sequence of input text, a tokenizer algorithm first converts the text into a machine-readable format by breaking down the text into small, discrete units called \u201ctokens” for analysis; tokens themselves are often single words or single letters.\u00a0<\/span><\/p>\n

For example, the sentence “Hello, world!” can be tokenized into [“Hello”,\u00a0 “,”,\u00a0 “world”,\u00a0 “!”].\u00a0<\/span><\/p>\n

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These tokens are then converted into numerical values known as embedding vectors, which is the format expected by the transformer model. However, because transformers can’t inherently understand the order of words, each embedding vector is combined with a positional encoding. This step ensures the order of the words is taken into account by the model.<\/span><\/p>\n

After the input text is tokenized, it is passed through the encoder to create attention vectors, which are numerical values that help the model determine the relevance and relationship of each token to the others in the input. This helps the LLM capture dependencies and relationships between tokens, giving it the ability to process the context of each token in the sequence.\u00a0<\/span><\/p>\n

The attention vectors are then passed to the decoder to receive an output embedding, which are then converted back into tokens. The decoder process continues until a \u201cSTOP\u201d token is output by the transformer, indicating that no more output text should be generated. This process ensures that the generated output considers the relevant information from the input, maintaining coherence and context in the generated text. This is similar to how a human might receive a question, automatically identify the most important aspects of the question, and give an appropriate response that addresses those aspects.<\/span><\/p>\n

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Strengths<\/b><\/h2>\n

Large language models exhibit several strengths that businesses can capitalize on:<\/span><\/p>\n