What Are Large Language Models? LLMs Explained In Plain English

The artificial intelligence industry is arguably as complex as it is exciting. With the recent introduction of large language models (LLMs), the capabilities of AI are expanding in both scope and awareness. While you need extensive training to work in this field, it can seem like a tall order just to understand the concepts.

Neural networks, machine learning, natural language processing… these are just some of the terms related to the study of language models. If you’re scratching your head over how LLMs work and what they can be used for, don’t worry. We’re going to break it down in plain English in this article.

What Are Large Language Models and How Do They Work?

In the most basic terms, a language model is a computer model centered around predicting text. The model is trained on sets of text. Then, when given some text as an input, the model assigns some probability of each word in the sequence of appearing.

Traditionally, language models have been mostly utilized in computational linguistics. This includes areas such as improving speech and handwriting recognition, as well as information retrieval. Language models are considered within the study of natural language processing (NLP). NLP is essentially concerned with how we communicate with computers using human language.

The key difference with LLMs is “large.” All language models are trained to recognize characteristics of input text, which are known as parameters. However, LLMs are trained with a much greater number of parameters, often in the billions.

These parameters form the basis of a neural network. This is basically a network with a structure based on the human brain. The basic units of a neural network are called perceptrons, which are organized in interconnected layers and process data non-linearly.

LLMs tend to use the transformer architecture, which enables the LLM to maximize the probability of the next assigned word. This allows developers to train the LLM in an unsupervised manner on vast amounts of textual data.

Over time, the LLM recognizes almost countless relationships between words. It’s due to this capacity to “learn” from data and improve the output that categorizes them as machine learning.

This souped-up type of language model has been around since 2018 and has drastically changed the trajectory of NLP. Instead of being focused on supervised and specific tasks, the applications are becoming more generalized.

What Can Large Language Models Be Used For?

Understandably, now that LLMs have been released commercially, much of the hype around them is to do with their uses. We’ll get into the most significant of these next.

Language Generation, Comprehension, and Translation

Unsurprisingly, one of the biggest uses of LLMs is around understanding and manipulating language. Some of the most popular LLMs so far, such as GPT-3, have a heavy focus on producing conversational text from the prompts given.

This has many uses in technical fields like translation, information retrieval, and sentiment analysis; the uses are only limited to your imagination. From content creation to writing formal letters and summarizing text, wherever the written word’s involved, there’s probably a way for LLMs to aid the process.

Answering Queries

Since LLMs are getting better and better at mimicking human conversation, using them as a form of customer support makes sense. If the model is trained on a sufficient knowledge base, it can pull from this information to answer consumer queries likely faster than a human could.

While rudimentary chat support has been around for a while, most of it leaves something to be desired in terms of accuracy and efficiency. This is an area where companies hope LLMs can improve massively.

Code Generation

While the current state of LLMs can’t write perfect code or completely replace a programmer, this is a field that shows a lot of promise. Most LLMs have been trained on data of such versatility that they’re relatively adept at generating example code and also providing explanations of lines of code.

Although errors are occasionally made, this is immensely helpful in producing example code quickly and helping developers to understand specific varieties of code easier.

How Good Are Current Large Language Models?

As with most things technical, there’s always room for improvement. LLMs are leaps and bounds above the language models of old in terms of accuracy and applications, but they are by no means perfect. Some of the limitations are listed below.

Can Be Inaccurate

LLMs are a lot more accurate than their predecessors and their training sources are substantial, but they’re still finite. This means that they can only be as accurate as the data they’ve been trained on. They can also only be as accurate as how correctly they can interpret this data.

While LLMs are being improved all the time, current renditions, like GPT-3, can produce confidently inaccurate results, including erroneous facts or references. A discerning eye is needed to pick up on these, as the information will often seem correct to a casual observer.

The Output Won’t Be Perfect

Whatever you decide to produce using an LLM, chances are it won’t be perfect as a finished product. Even without potential factual errors, the text produced can be rather formulaic and predictable, especially if your initial prompts are relatively vague and not descriptive.

To obtain a better result, you’ll want to be as specific as possible with your guidelines, as well as generate multiple responses. That way, you can get a feel for the key messages to take away, as well as optimize your outputs.

Biased Responses

Since these models are trained on datasets, as mentioned before, they can only be as objective as the data. Therefore, biases present in data tend to become absorbed by the model, including social and cognitive biases.

These models are trained to avoid producing inappropriate, harmful, or illegal content. However, the risk of this increases as they become more powerful and they’re trained on more data. Moderation of content, as well as refining training data, are both crucial methods that must be used to mitigate this.

Wrapping Up

Overall, there’s still a long way to go in regard to improving and enhancing large language models. Much of these efforts will center around making outputs more accurate and less harmful. However, the excitement around LLMs can’t be understated.

This has been especially true within the last year, due to the emergence of publicly accessible generative models. As these models continue to be developed and adopted widely, we’ll likely see big shifts in the way we live, play, and work.

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