What is a large language model?

A large language model (LLM) is a deep learning algorithm that can perform a variety of natural language processing (NLP) tasks. Large language models use transformer models and are trained using massive datasets — hence, large. This enables them to recognize, translate, predict, or generate text or other content.

Large language models are also referred to as neural networks (NNs), which are computing systems inspired by the human brain. These neural networks work using a network of nodes that are layered, much like neurons.

In addition to teaching human languages to artificial intelligence (AI) applications, large language models can also be trained to perform a variety of tasks like understanding protein structures, writing software code, and more. Like the human brain, large language models must be pre-trained and then fine-tuned so that they can solve text classification, question answering, document summarization, and text generation problems. Their problem-solving capabilities can be applied to fields like healthcare, finance, and entertainment where large language models serve a variety of NLP applications, such as translation, chatbots, AI assistants, and so on.

Large language models also have large numbers of parameters, which are akin to memories the model collects as it learns from training. Think of these parameters as the model’s knowledge bank.

How do large language models work?

A large language model is based on a transformer model and works by receiving an input, encoding it, and then decoding it to produce an output prediction. But before a large language model can receive text input and generate an output prediction, it requires training, so that it can fulfill general functions, and fine-tuning, which enables it to perform specific tasks.

Training: Large language models are pre-trained using large textual datasets from sites like Wikipedia, GitHub, or others. These datasets consist of trillions of words, and their quality will affect the language model's performance. At this stage, the large language model engages in unsupervised learning, meaning it processes the datasets fed to it without specific instructions. During this process, the LLM's AI algorithm can learn the meaning of words, and of the relationships between words. It also learns to distinguish words based on context. For example, it would learn to understand whether "right" means "correct," or the opposite of "left."

Fine-tuning: In order for a large language model to perform a specific task, such as translation, it must be fine-tuned to that particular activity. Fine-tuning optimizes the performance of specific tasks.

Prompt-tuning fulfills a similar function to fine-tuning, whereby it trains a model to perform a specific task through few-shot prompting, or zero-shot prompting. A prompt is an instruction given to an LLM. Few-shot prompting teaches the model to predict outputs through the use of examples. For instance, in this sentiment analysis exercise, a few-shot prompt would look like this:

Customer review: This plant is so beautiful!
Customer sentiment: positive

Customer review: This plant is so hideous!
Customer sentiment: negative

The language model would understand, through the semantic meaning of "hideous," and because an opposite example was provided, that the customer sentiment in the second example is "negative."

Alternatively, zero-shot prompting does not use examples to teach the language model how to respond to inputs. Instead, it formulates the question as "The sentiment in ‘This plant is so hideous' is…." It clearly indicates which task the language model should perform, but does not provide problem-solving examples.

Large language models use cases

Large language models can be used for several purposes:

• Information retrieval: Think of Bing or Google. Whenever you use their search feature, you are relying on a large language model to produce information in response to a query. It's able to retrieve information, then summarize and communicate the answer in a conversational style.

• Sentiment analysis: As applications of natural language processing, large language models enable companies to analyze the sentiment of textual data.

• Text generation: Large language models are behind generative AI, like ChatGPT, and can generate text based on inputs. They can produce an example of text when prompted. For example: “Write me a poem about palm trees in the style of Emily Dickinson.”

• Code generation: Like text generation, code generation is an application of generative AI. LLMs understand patterns, which enables them to generate code.

• Chatbots and conversational AI: Large language models enable customer service chatbots or conversational AI to engage with customers, interpret the meaning of their queries or responses, and offer responses in turn.

In addition to these use cases, large language models can complete sentences, answer questions, and summarize text.

With such a wide variety of applications, large language applications can be found in a multitude of fields:

• Tech: Large language models are used anywhere from enabling search engines to respond to queries, to assisting developers with writing code.

• Healthcare and Science: Large language models have the ability to understand proteins, molecules, DNA, and RNA. This position allows LLMs to assist in the development of vaccines, finding cures for illnesses, and improving preventative care medicines. LLMs are also used as medical chatbots to perform patient intakes or basic diagnoses.

• Customer Service: LLMs are used across industries for customer service purposes such as chatbots or conversational AI.

• Marketing: Marketing teams can use LLMs to perform sentiment analysis to quickly generate campaign ideas or text as pitching examples, and much more.

• Legal: From searching through massive textual datasets to generating legalese, large language models can assist lawyers, paralegals, and legal staff.

• Banking: LLMs can support credit card companies in detecting fraud.

Benefits of large language models

With a broad range of applications, large language models are exceptionally beneficial for problem-solving since they provide information in a clear, conversational style that is easy for users to understand.

Large set of applications: They can be used for language translation, sentence completion, sentiment analysis, question answering, mathematical equations, and more.

Always improving: Large language model performance is continually improving because it grows when more data and parameters are added. In other words, the more it learns, the better it gets. What’s more, large language models can exhibit what is called "in-context learning." Once an LLM has been pretrained, few-shot prompting enables the model to learn from the prompt without any additional parameters. In this way, it is continually learning.

They learn fast: When demonstrating in-context learning, large language models learn quickly because they do not require additional weight, resources, and parameters for training. It is fast in the sense that it doesn’t require too many examples.