AI Writing
What Are Large Language Models (LLMs): An Overview
Discover what large language models (LLMs) are. Plus, get insight into how AI detection helps maintain transparency with the rise of generative AI content.
For many, one of the most confusing aspects of generative AI and its expansion into the mainstream is the terminology.
After all, given how new the technology is to most of us, terms like ‘natural language processing,’ ‘machine learning,’ and‘ search generative intelligence,’ may not be very familiar.
But perhaps the most common phrase AI enthusiasts will hear is large language models (LLMs).
Platforms often reference using them as part of their service, but very few dive into detail about what they actually are.
In this article, we will break down large language models, so you can keep learning more about artificial intelligence.
Let’s dive straight in and answer your top question. What are large language models (LLMs)?
According to Amazon Web Services, large language models also referred to as LLMs for short, are essentially extensive deep-learning models.
Now, that might sound like quite a complex answer, so here’s an example.
Let’s say, someone is searching for a new bass. This could refer to either bass the instrument or bass the fish.
Both words are spelt exactly the same, which is why it’s crucial that LLMs have the necessary training and enough data to ensure the tools can ‘read between the lines’ if you will and understand the context of what you are asking.
So, using that example, if a user asks ChatGPT, “give me the top 10 places to find a bass,” the tool will make a decision on whether or not they are likely to be looking for fishing spots or music stores. In the same way, an LLM-powered PDF annotation tool could analyze text and highlight relevant mentions of "bass" based on the context of the document.
Here’s the result:
Of course, this is a rather extreme example, and others are much more nuanced, but it gives you an idea of the process that takes place.
The large language model reads the request, scours its vast dataset, and makes decisions on which question the user is asking.
This brings us to another common question about LLMs. Is ChatGPT a large language model?
The answer is yes; ChatGPT is one of the most popular examples of a large language model (read more about foundational LLM models).
As demonstrated in the example above, ChatGPT uses its vast data and training to answer consumer queries to the best of its ability.
It’s also important to note that ChatGPT is subject to a knowledge cutoff date. This is the reason why ChatGPT isn’t always as good at answering questions on the most up-to-date topics, as it has yet to consume the most recent data required to provide a good answer.
When ChatGPT was first launched in the mainstream, the gap between knowledge and current events was dated to September 2021 as noted by the Poynter Institute.
As of the publication of this article, the knowledge cutoff date is now October 2023 for GPT-4o, confirmed by OpenAI.
Another common question is: How can you maintain transparency with text generated by LLMs?
Although humans might have confidence that they can identify AI, several studies have found that humans struggle to identify AI text. This is where AI detection comes in.
Originality.ai offers a best-in-class and industry-leading AI Detector. Using an AI detector as part of the editorial process helps to establish and maintain transparency around the content published, whether you’re a content marketer, web publisher, or editor.
When you run an AI detection scan with text generated by a popular LLM such as GPT-4o, Gemini Pro, or Llama 3.1 you’ll receive an AI detection score indicating the confidence that the text is Likely AI or Likely Original.
To learn more about how AI content detection works check out our top guides:
Each AI content detector has its own approach to AI content detection, with some more effective than others. Read our round-up of AI detector reviews to compare different AI checkers.
Large language model accuracy depends on the task in question, the quality of the prompt, and the depth of its training data.
One of the top issues with generative AI is that it can produce AI hallucinations. AI hallucinations occur when a generative AI model generates an inaccurate statement that it presents as a fact. So, it’s important to fact-check and review content carefully.
Large language models are trained on extensive datasets and if the data contains bias it could replicate this in its generations. This has made AI governance and responsible AI an increasingly important focus in AI development.
There are pros and cons to generative AI. That said, when used effectively they can be incredibly useful tools for making certain tasks more efficient. For instance, AI can be beneficial from a content marketing perspective for ideating topics or conducting initial research.
Just keep in mind, that the best practice when incorporating LLMs into your workflow is to maintain transparency. For instance, when writing content, review AI guidelines and policies right from the beginning of a writing contract before submitting work, so that everyone is on the same page.
