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Large Language Models explained

Last updated 20 February 2024

What are large language models?

Large language models, or LLMs, are a type of AI that can mimic human intelligence. They use statistical models to analyze vast amounts of data, learning the patterns and connections between words and phrases. This allows them to generate new content, such as essays or articles, that are similar in style to a specific author or genre.

As technology advances, we are constantly discovering new ways to push the boundaries of what we thought was possible. Large language models are just one example of how we are utilizing artificial intelligence to create more intelligent and sophisticated software. In this blog, we will take a closer look at what LLMs are, and how they work.

How does a Large Language Model work?

To understand how large language models work, it's helpful to first look at how they are trained.

The efficacy of an LLM comes largely from its training process. During this phase:

  • Pre-training: The model is exposed to massive amounts of text data (such as books, articles, or web pages), so that it can learn the patterns and connections between words. The more data it is trained on, the better it will be at generating new content. While doing so, it learns to predict the next word in a sentence. For instance, given "The cat is on the ___," the model might predict "mat.". Through billions of such predictions, the model learns grammar, facts about the world, reasoning abilities, and even some biases present in the data.
  • Fine-tuning: After the initial pre-training, the model can be refined on a narrower dataset to specialize in specific tasks or knowledge areas. This helps in aligning the model's outputs with desired outcomes.

How can Large Language Models be used?

Once the large language model has been trained, it can be used to generate new content based on the parameters set by the user:

  • Completion: Given a partial sentence, it can predict the next words.
  • Question answering: You ask a question, and it generates a relevant answer.
  • Translation, summarization, and more: The model can be adapted for various text generation tasks.

For example, if you wanted to generate a new article in the style of Shakespeare, you would provide the Large language model with a prompt, such as a sentence or paragraph, and it would generate the rest of the article based on the patterns and connections it has learned from analyzing Shakespeare's works.

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Large language models can also be used for a wide range of other applications, such as chatbots and virtual agents. By analyzing natural language patterns, they can generate responses that are similar to how a human might respond. This can be incredibly useful for companies looking to provide customer service through a chatbot or virtual agent, as it allows them to provide personalized responses without requiring a human to be present.

What are the challenges of Large Language Models?

Of course, like any technology, large language models have their limitations. One of the biggest challenges is ensuring that the content they generate is accurate and reliable. While LLMs can generate content that is similar in style to a particular author or genre, they can also generate content that is inaccurate or misleading. This is particularly true when it comes to generating news articles or other types of content that require a high degree of accuracy.

One way of mitigating this flaw in LLMs is to use conversational AI to connect the model to a reliable data source, such as a company’s website. This makes it possible to harness a large language model’s generative properties to create a host of useful content for a virtual agent, including training data and responses that are aligned with that company’s brand identity.

Yet, due to the innate unpredictable nature of these models, achieving absolute 100% accuracy is presently unattainable. It's essential to have a human review and verify the outputs of large language models prior to sharing with end-users. This step is especially vital in business environments where there might be potential liability issues.

At boost.ai, we have developed proprietary algorithms that are able to accurately scan websites and other data sources, and interface with LLMs to essentially tame them for use in enterprise use cases.

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