Large language model (LLM)

In today's digital era, artificial intelligence is evolving at an unprecedented pace, and one of its revolutionary developments is the large language model (LLM). Learn about what an LLM is, how it works, how it's relevant, its advantages, and its limitations.

What is a large language model?

A large language model (LLM) is an AI model that uses deep learning techniques to process and understand vast amounts of text data. The large size is what gives it a unique characteristic over regular language models.

The model uses the Transformer architecture, a neural network architecture that transforms input data from one representation to another. It also uses an attention mechanism—which helps it focus on certain text elements and block out other aspects—and a standard pooling/encoding layer to identify patterns.

Large language models cannot be run on a single computer due to the immense computing power required. LLMs have been trained through deep learning algorithms to recognize language patterns, contributing to their ability to perform tasks like text generation, translation, summarization, and more.

How do large language models work?

The core of LLMs is a type of model called a transformer. Transformers work by taking in a sequence of words or characters, processing them all at once (or in parallel), and then outputting another sequence. They use a mechanism called attention, which allows the model to weigh the importance of different words or characters in the input when generating the output.

LLMs must be trained on vast amounts of text data to predict the next word in a sentence using what is called maximum likelihood estimation. Once trained, LLMs can generate new text that is similar in style and content to the text they were trained on. They do this by taking an input—such as a prompt from a user—processing it through the model to generate a distribution over possible next words and then sampling a word from this distribution.

Why do people use large language models?

The predictive text features of large language models that mimic human communication make them useful for a variety of industries and use cases.

People use LLMs primarily because of their capabilities in natural language processing tasks such as translation, summarization, sentiment analysis, and more. For instance, companies that operate globally might use LLMs to automatically translate text between languages, allowing them to communicate effectively with customers around the world.

Another common use of LLMs is in chatbots and virtual assistants. These AI-powered tools use LLMs to understand user queries and generate relevant responses. This can greatly enhance the user experience by providing swift and accurate customer service, making LLMs a valuable asset for retail, hospitality, and healthcare businesses

The advantages of large language models

All the advantages of large language models center around natural language processing and human text. ChatGPT is the latest advancement that has popularized large language models.

The biggest advantage is the improved ability to understand and generate human-like text. They are trained on extensive databases encompassing a wide variety of topics, styles, and structures. This vast training allows them to comprehend human language context, nuances, and subtleties, helping them respond appropriately.

Unlike smaller models that are often designed for specific tasks, large language models are incredibly versatile. They can be used for a wide range of applications, from chatbots and virtual assistants to content generation and language translation.

Large language models can automate several time-consuming tasks, leading to increased efficiency and cost savings. For instance, they can help businesses automate customer service, content creation, and document review, among other tasks. Moreover, these models can operate 24/7, ensuring round-the-clock service.

Limitations of large language models

While large language models have transformed various aspects of the digital landscape, they do come with limitations.

Despite their ability to generate human-like text, large language models don't truly understand the content they're processing in the same way humans do. They generate responses based on patterns they've learned during training, not because they comprehend the underlying concepts or context.

They can't verify the accuracy of information or update it with real-time data. For example, they can't provide the latest news or stock market updates unless they've been specifically designed to pull in such data.

Since these models learn from vast amounts of data available on the internet, they risk absorbing and reproducing the biases present in those datasets. This can lead to outputs that are discriminatory, offensive, or inappropriate. Mitigating these biases is a significant challenge that requires ongoing research and careful model design.

Finally, training large language models requires significant computational resources and energy, contributing to environmental concerns. Additionally, the cost of training these models can be prohibitive, limiting their accessibility.