What Are the Different Types of Language Models?

What Are the Different Types of Language Models?

ChatGPT, Natural Language Processing, Machine Learning—so many buzzwords flying around, but what do they all really mean? When we hear about artificial intelligence, especially in the context of AI chatbots, it can be really confusing to learn and understand how any of it really works, or what’s going on behind the scenes. A lot of times with this tech stuff, the actual tech behind it is a bit of a black box.

Learning a little bit about it doesn’t have to be incredibly difficult, though. Chatbots like ChatGPT function on a tool called Natural Language Processing (NLP), which is the way these programs are able to understand and interpret human language and respond to incomprehensible language. NLP models can typically be sorted into a few different categories. One way to sort them is to consider statistical models or neural network models.

Statistical Models

Statistical models and neural network models generally partition the NLP models, and statistical models are considered to be the simpler of these two. Essentially, a statistical model for Natural Language Processing is built around n-grams, which refer to a sequence of n words. Trained on different collections of texts, a statistical model calculates how likely a word is to come after the previous n-1 words. For example, a bigram (2-gram) model calculates the most likely word to come after every single word in a sentence in construction. A trigram (3-gram) calculates the most likely word after each pair of words in a sentence in construction.

Through knowledge of word frequency alone, a statistical model constructs sentences based on how often it has seen a specific word appear in text after a certain number of previous words. This class of models has plenty of strengths and weaknesses to consider. Regarding strengths, it’s a relatively simple model to construct and implement. This also makes it a pretty fast model to run, and it’s quite efficient as far as memory usage goes. However, statistical models have some pretty serious limitations. For one, frequency learning alone does little-to-nothing to understand the relationship between different words, meaning that there is no nuanced understanding of the semantic meanings of the language it is reconstructing. If nothing else, this makes the language feel less natural, and it bars the ability to flesh out longer, more nuanced ideas.

Neural Network Models

Neural networks are the more complex of the two, and they’re more likely to be the models you’re going to run into in a lot of the popular software available right now. Without getting too much into the more complicated mathematical structures, neural networks are built around high-dimensional vector fields that allow the model to form a more complex and nuanced understanding of how different words relate to one another. In a much oversimplified way, neural networks make an effort to extrapolate some kind of meaning or semantic relation between words, as opposed to statistical models that simply look to see what the most likely following word would be based on observed frequencies.

As might be expected, the pros and cons around neural network models are totally related to complexity, especially in relation to statistical models. Neural networks are able to form deeper understandings of linguistic patterns and, in consequence, are able to return more interesting, comprehensive, and nuanced responses. Generated text can be constructed holistically and purposefully, not just as a likely string of words that eventually has to end. However, a model like this is significantly more complicated, which makes it more difficult to build, more difficult to implement, and more expensive to execute. Also, since the relationship between words is more complex than just statistical likelihood, it can sometimes be more difficult to understand what links are being made, and how they’re being used. This can make it difficult to fix or work on a neural network model, whereas a statistical model’s choice of following words should be pretty straightforward.

Other Categorizations

The statistical/neural network binary is not the only way we can classify language models. We can also do so in terms of how the models are trained or, more specifically, on what kind of data is used to train the models. In particular, we might consider private and public language models. This categorization is a bit more straightforward. Language models can either be trained on data sets that are public, available to anybody who wants them and contain data representative of the general public. Or they can be trained on data that is private, perhaps specific to a single company or brand, and only representative of a specific company’s culture, language, and protocol.

Again, these two models have their strengths. Public models tend to be more versatile, and tend to serve wider scopes. ChatGPT, for example, is trained on an enormous public dataset as it is meant to answer general questions about nearly anything within the public domain. However, it cannot disclose sensitive or private information, if it is aware of it at all. On the other hand, private models are trained on private data for very specific scopes. A company may have an AI chatbot to help employees navigate internal data, so the language model will need to be trained on company information, but won’t necessarily have to know everything about the public world. When chatbots pop up on brand sites, you probably won’t get much information out of them regarding your favorite musical artist, for example.

There are several kinds of categorizations that we can consider when trying to understand how different language models work, and what purposes they serve. With this quick peek behind the scenes, though, hopefully, the tech behind the ubiquitous AI bots and models seems like a bit less of a black box, and more just different implementations of the same overarching concept: machine learning.