In the ever-evolving world of technology, there's a term that's been buzzing around more than a bee in a garden full of flowers: Large Language Models, or LLMs for short. Now, if you're picturing a giant dictionary flying around in cyberspace, you're not entirely off the mark, but there's a bit more to it. LLMs are relatively new, with the first versions only appearing about 5 years ago. ChatGPT is the most well known LLM and has found it's way into most homes and offices by now. There are a huge amount of things that LLMs can do today, draft emails, presentations, memos, even blog posts. To see how this relates to generative AI vs agentic AI, read our companion post.
Understanding what LLMs are is extremely helpful because they are increasingly shaping the way with interact with technology, from simplifying our daily tasks to transforming (or creating!) entire industries. For a non-specialist or someone not working with technology, grasping the basics of LLMs clarifies how devices and applications can seemingly "think" and communicate in human-like language, offering insights into how reliable they are, potential risks for bias and the limitations of information and services provided by AI.
Being able to critically analyse LLMs, and other AI-based technology that use them, will be a key skills that all professionals will need to develop over the coming years. 'The AI did it' will not be a defence to be relied upon should things go south!
Understanding Large Language Models
A Large Language Model can be likened to an extremely well-read friend who possesses knowledge on a wide array of subjects, ranging from the literary works of Shakespeare to the intricacies of quantum physics. Essentially, an LLM is a sophisticated software program that has been trained on huge amounts of textual content (text), enabling it to comprehend and generate human-like language. The term "large" reflects the vast volume of data it processes to acquire its abilities to understand and generate language.
The Operational Mechanics of LLMs
To comprehend how LLMs work, one might imagine them as sponges absorbing linguistic information. They process diverse textual content from books, websites, newspapers, and other sources. Through the analysis of this data, they discern patterns, structures, and nuances of language.
When prompted with a question or a request for textual content, an LLM retrieves its acquired knowledge on how words and phrases are typically combined. It then employs this understanding to formulate sentences that align with the given request, mirroring, for example, a conversation with a knowledgeable friend who can generate narratives or explanations on the spot.
Where Do They Find this Data?
LLMs to date have put together enormous repositories of text, usually meaning billions of pages scraped (copied) from the internet, like blog posts, tweets, Wikipedia articles and newspaper articles. Most LLMs start off with free, publicly available data libraries, but might turn to protected or pay-walled content (such is what the New York Times alleges in a recent lawsuit against OpenAI). In general, the more data there is, and the more sources it is drawn from, the better our model will be.
The Learning Process of LLMs
LLMs acquire their capabilities through a method known as machine learning, wherein they automatically identify patterns in the data that has been fed to it. Unlike traditional learning scenarios, no direct instruction is involved; the model learns autonomously by analysing textual data. Furthermore, the more data it sees, the better it gets at predicting and generating language that sounds natural to humans, known as Natural Language Processing (NLPs).
The Possibility of Errors
Despite their extensive knowledge base, it is crucial to acknowledge that LLMs are not perfect and possess many flaws. They are susceptible to errors, misconceptions, or the generation of inaccurate (or outright fake) information. This is attributed to their reliance on pattern recognition within their training data, rather than a genuine comprehension of the world the way humans do. Consequently, while LLMs can be extraordinarily useful, it is advisable to verify the accuracy of the information they provide.
Ethical Considerations
The development and deployment of LLMs raise significant ethical considerations, including concerns about privacy, the potential for bias in training data, and the risk of misuse.
It is imperative for both developers and users to approach these issues thoughtfully, striving to use these models for the greater good. For a deep dive on the ethical considerations of AI, read our AI literacy post!
End of export. 20 posts, generated by Claude for Sarah at DEDICO Ltd.
Frequently asked questions
What is an LLM?
A software program trained on huge amounts of text to comprehend and generate human-like language.
Are LLM outputs always accurate?
No. LLMs rely on pattern recognition, not understanding, so they can produce inaccurate or fabricated content and should be verified.