Artificial intelligence is expanding across many disciplines and industries. The most compelling evidence of that expansion is in language. Large language models are reshaping the way we interact with technology. They are also altering our perception of what machines are capable of and how we communicate with them, whether on a desktop,smart speaker, or Android phone.
You may have heard of some of the leading models, like GPT3, which is a generalized LLM that isn’t tailored to a specific task or industry. There are a growing number of domain-specific LLMs with a narrow, focused intent.
What is a large language model?
Before we delve into domain-specific LLMs, let’s start with a top-level definition of the term large language model. A language model, likeMicrosoft’s Turing Natural Language Generation model, is an artificial intelligence model that processes and generates language.
A common example of language model applications is machine translation, where the model translates text or speech from one language to another, or speech recognition, where a machine identifies, processes, or transcribes spoken language. Autocomplete is another popular application of language model technology, where the model predicts a word or phrase based on limited text input.
What are large language models?
Large language models (LLMs) are the basis for AI chatbots and much more. Here’s what’s going on behind the scenes
Large language models are the most advanced, capable, and complex version of language models. LLMs combine larger datasets, typically to broadly understand and generate speech, text, or language. They’re often trained on information scraped from the internet, transformers (a type of deep learning model introduced in 2017), or artificial neural networks (machine learning models using principles derived from how human and animal brains are structured).
Large language models are capable of sophisticated language processing, often in real time, allowing them to answer questions, generate contextually appropriate text like prose or poetry based on user prompts, power chatbots, analyze text for tone, or identify thematic elements.
What are domain-specific LLMs?
Standard large language models like GPT3 are general purpose, with no specific focus on function, training, or intent. Domain-specific LLMs are trained in a narrower band of data to bring them to a higher, specialized level of expertise in a single subject or handful of subjects. They outperform general LLMs in subject-specific benchmarks but are often less capable in broad language processing tasks.
In large language models, the term domain doesn’t refer to an internet domain name. It’s an industry, field, or specialized area of expertise.
What are the advantages of domain-specific LLMs?
Domain-specific LLMs are intensively trained on narrow data sets and tend to be more capable within their area of expertise than generalized models. Within their field, they’re more capable of providing relevant and actionable information. They also tend to handle an industry’s specific jargon and terminology more accurately than general-purpose LLMs.
Another advantage of domain-specific LLMs is the reduced resource outlay to train them. Standard LLMs require huge datasets and a tremendous amount of training. The datasets used to train domain-specific LLMs are smaller and can be trained faster and cheaper. For a similar reason, they can make faster inferences and respond to user queries or prompts quicker.
What are the applications of domain-specific LLMs?
The potential applications of domain-specific LLMs are broad and have only begun to be broached. Domain-specific LLMs are experts in their fields. They offer high-level data processing, analysis, or advice in near real time.
LLMs trained in law and jurisprudence can draft contracts, analyze rulings, and assist in case research. Medical LLMs can search and collate patient records, suggest treatments, or assist in diagnosis. An LLM trained in finance and market analysis can analyze market trends, condense financial reports from multiple sectors into digestible and actionable data, or rate potential investments.
There are possibilities for the application of domain-specific LLMs in content creation, which we’re seeing in areas like the visual arts, creative writing, and journalism.
Building expert machines
Domain-specific LLMs hold the potential to revolutionize almost every industry, transforming how we think about expert knowledge and the accessibility of complex data. Any field that relies on information analysis, which nearly every field does, can be altered by deploying AI and domain-specific LLMs.
It’s not all roses. For a look at the darker side of artificial intelligence’s potential, read about how the use ofAI in search verges on plagiarism.
