Understanding the large language model

We bump into AI almost all the time nowadays. It has evolved to be part of our everyday lives as it can help us streamline our work and private life. One of the terms connected to AI is large language model which we don’t really hear about – but it’s one of the cornerstones of the mind-blowing technology.

What is a language model?

We should start off with the basic language model that has become the basis for many modern technological systems.

A language model uses different statistics and probability theories to calculate a sequence of words in a given context. The language model thus analyzes a big data set to make a basis for word predictions and sentence compositions.

Language models are often used in AI technology and natural language processing, understanding and generation. Since it’s based on text, we usually see it in text-based programs such as:

• Machine translation
• Chatbots
• Question/answer software

Language models use text to determine word probability – with the help of algorithms, it can analyze natural language and mimic this in user interaction. It basically learns how we write and produce structured sentences and replicate the structure. If the language model is presented with a new phrase it will understand the meaning and structure quickly since it has a foundation in the data set it has gathered.

A bigger data set

You might already have guessed it, but the large language model is trained on enormous sets of data – hence the word "large". LLM is based on machine learning. ML is:

• A type of algorithm that can learn through statistical analysis, without any definite prompts or instructions. It can thus do different tasks independently from a user, as it generalizes its content. ML is part of AI, which refers to a computer’s artificial intelligence, i.e. a way for it to "think" or mimic human cognition.

The bigger data set within LLM enables it to understand and analyse text a lot quicker than regular LM. It can thus interpret the human language and more complex data which can make it seem that we’re talking with another human being and not a machine.

The data, that LLM bases its information on, is gathered from the internet and the billions of websites it contains. This means that there might be some quality issues if you use unlimited data – some programmers try to limit the websites through sorting algorithms to get a better and less biased outcome from the language model.

LLM uses deep learning to understand human language and its composition. It learns how sentences function together and to analyze context.

How can we use LLM?

You might sit and wonder what we can use LLM for and how we can use the impressive technology. As a private user, we can use generative AI technology, e.g. with chatbots like ChatGPT, Microsoft’s Bing Chat, Meta’s Llama and Google’s Gemini.

We can ask the chatbot to give us a workout plan, a weather forecast or a movie review. It’s only our imagination that sets the limit for what we can ask chatbots. They have, fortunately, been programmed to avoid any malicious use. Hackers did try to exploit the chatbots to create malware and coding for this, but developers of the different chatbots have created improved filtering of malicious prompts.

Not only can private users access and use AI for their entertainment and comfort; institutions like healthcare can use AI and LLM to analyze and understand patient journals and treatment plans.

• LLM can generally be used for analysis of a larger data set which can help many people in the future.

Pros and cons of LLM

Large language models seem like a great and helpful feature in modern technology – it simplifies our communication with AI and enables us to use technology in the best possible ways.

LLMs can respond to any prompt we give it – while it understands natural human language and can compose structured sentences that are relevant and make sense to the user.

However, we have already briefly touched upon one of the disadvantages of this great technology, namely, where it gets its information. Many users do not filter their search or adjust the settings in the AI software they’re using. The chatbots will e.g. collect data from around the internet; LLM cannot distinguish between fact and fiction. This means that you can get a response that is not entirely true.

If the chatbot doesn’t know the answer to the prompt i.e. doesn’t have the needed information, it will make up something that might sound probable and true. An example is the news outlet, Fast Company, which asked OpenAI’s chatbot about Tesla’s economic year, to which the chatbot gave a thorough description of the financial quarter – however, most of the information it gave the journalist was false.

• This emphasizes that we need to be cautious when we use AI and not trust everything and every fact it serves us.

Another element we should be cautious of when it comes to LLM and AI is the software and user surface. Just like any other online technology, it can have its vulnerabilities and they will often be exploited if they aren’t fixed immediately by its developers.

We mentioned the filtering system within LLM software earlier, and it has come a long way. However, it is impossible for the program’s developers to find each and every flaw and weak code in the software. This means that the gaps in the filters will be exploited, and hackers will continue to try to use chatbots to make malicious codes and malware that they can spread on our devices.

The large language model is an impressive tool that has created the foundation for an even more impressive technology with artificial intelligence. With the continuing evolution of AI and LLM, we might reach a point where the technology solely gives us correct answers to our questions and rarely makes a mistake when helping us with our everyday endeavors.