Top Large Language Model Types

Many innovations have emerged with the increase in technological innovations and artificial intelligence. Large language models are also among them. Many individuals believe that LLM is only a short-term trend that will soon vanish. 

But is that true? Not at all. 

According to a recent study, Large Language Models are becoming more advanced daily. Compared to previous models, the latest LLM models show a 15% increase in efficiency in natural language understanding tasks. It also shows us the progress of AI and how it’s revolutionizing different tasks. 

Having a surface-level understanding of LLMs is not enough. You need to know more about it. In this article, we’ll explain some of the most important Large Language Model types you should know. 

So let’s get started. 

What Is a Large Language Model?

A large language model is an advanced artificial intelligence model. It excels in natural language processing tasks. These models easily comprehend and generate texts. But that’s not robotic text, which is generally hard to understand. 

Instead, it’s a human-like text based on patterns and structures. The models learn from vast training data. So far, LLMs are used in various applications related to language. Some are text generation, translation, summarization, handling customer queries, etc. 

The core of LLMs is deep learning architecture, also known as transformer. The transformers consist of various layers of self-attention mechanisms. It allows the model to weigh different words or tokens. So, in a nutshell, LLMs can effectively process and generate texts with contextually relevant and distinct patterns.  

Types of Large Language Models

Now that you know what a large language model is. Let’s look at some of the most common types of LLMs. 

Based On Architecture

The three primary LLM types based on architecture are as follows. 

Transformer-based Models

These models are a class of neural network architectures. They rely on self-attention mechanisms to gain long-range dependencies in sequential data efficiently because of an increase in natural language processing tasks due to the ability to process input sequences in parallel. It makes them highly scalable and effective for tasks that require contextual understanding and text generation. 

Key Characteristics

Now that you know about the transformer-based model, let’s look at some key components. 

The core component of this model is self-attention. It weighs the importance of each word in a sequence. Thus understanding the contextual relationship between them. 

It consists of an encoder and a decoder. But what’s the significance?

Encoder = Processes input sequences

Decoder = Generates output sequence 

To improve representational capacity, the procedure of self-attention is repeated multiple times. It allows models to learn different parts of the input sequences simultaneously. 

Transformers do not inherently understand the orders of tokens. For this, positional encoding is added to the input embeddings

Applications

This model is widely used in a wide range of NLP tasks, some of these are as follows:

• Language Translation
• Text summarization
• Sentiment analysis
• Named entity recognition

Hybrid Models

Hybrid models combine various neural network architecture elements. It strengthens every approach of hybrid models. 

These models can address specific challenges and limitations in individual architectures. Thus leading to better performance and generalization across various tasks.

Key Characteristics

These models integrate critical components from transformers, RNN-based models, and other architectures. It helps achieve a balance between computational efficiency and expressive power.

A unified framework is adopted in this, which can complete several NLP tasks. Seamless integration of components and model development becomes simple with hybrid models. These are further fine-tuned on specific tasks using labeled data. 

Applications

Here are some of the applications of hybrid models. 

• Text classification
• Sequence labeling
• Text generation
• Language translation

RNN-based Models

RNN is also known as Recurrent Neural Networks. It’s a class of neural network architectures that process sequential data by maintaining an internal state or memory. 

They can capture temporal dependencies. But is that all?

No. It also collects sequential patterns in text data, which makes NLP tasks smooth. 

Key Characteristics

It has recurrent connections through which information persists over time. The hidden state of RNN acts as a memory that retains information from previous time steps. The overall update is done recursively at every step based on current input and hidden data. 

But there’s a major concern about vanishing gradients. Advanced variants such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) were introduced to resolve this. Gating mechanisms in these architectures regulate the flow of information. That’s how RNN-based models effectively collect long-term dependencies.

Applications

Now that you know about the key characteristics of RNN-based models, let’s look at some of the tasks they can perform.  

• Language modeling
• Sentiment analysis
• Machine translation
• Speech recognition

Based On the Training Approach

Here are three LLM types based on the training approach you should know about. 

Fine-tuned Models 

Fine-tuned Models are pre-trained neural networks that are further trained. To be more precise, they are fine-tuned on specific tasks with labeled data. The process includes adjusting and changing the parameters of pre-trained models. But why is that important?

It’s to better suit the requirements of the target task. It’s extremely helpful in contributing to better performance. 

Key Characteristics

They are typically initialized with parameters. They learn it during pre-training on extensive data. It sets a starting point for training on different tasks. The task-specific adaptation makes it an ideal choice for many applications. Here’s how the process continues. 

Adjusting learning rate → Training duration → Various parameters adjustment → Performance optimization 

It uses the power of transferring learned knowledge to improve performance on relevant tasks. Organizations that properly utilize this model achieve exceptional performance with less labeled data. 

Applications

This model is used to carry out various tasks. Here are some of them. 

• Natural language understanding
• Natural language generation
• Computer vision
• Reinforcement learning

Self-supervised Learning Models

It runs on unlabeled data with the help of self-supervised learning objectives. The model is tasked with the prediction of certain properties of input data. 

However, there’s no direct supervision involved. During this time, the models learn to extract crucial and meaningful data from raw data.  

Key Characteristics

This model uses unlabeled data, which includes images, texts, and audio. It predicts specific properties of input data. That’s how a model learns to collect meaningful information and structure inherent in the data. 

There are various common objectives for self-supervised learning. One of them is masked language modeling. In this, the model predicts masked or missing tokens in the sequence. Likewise, contrast learning and transfer learning are also utilized. 

Applications

Now that you know about self-supervised learning models, here are some applications. 

• Natural language understanding
• Speech recognition
• Representation learning
• Unsupervised feature learning

Supervised Learning Models

It is extensively trained on labeled data. But here’s the unique aspect. 

The input data is paired with the corresponding output labels or targets. While training, the model learns how to map input data to output labels. It happens while minimizing a loss function, which is usually predefined. It includes cross-entropy loss or mean squared error.

Key Characteristics

In this, every input sample relates to an output label. The training of models is compared with ground truth labels. It is possible with the usage of the predefined loss function. 

Based on this model, parameters are updated through backpropagation. 

Applications

Here are some of the tasks they can perform across several domains. 

• Image classification
• Sentiment analysis
• Named entity recognition
• Regression analysis

Based On Capabilities

These are the common LLM types when it comes to capabilities. 

Generative Models 

These are a class of machine learning models which are designed to generate new data samples. But these are similar to the training data. These models learn from existing training data and use it to generate new patterns with similarities. It is among one of the most popular models. 

Key Characteristics

These models learn the probability distribution of training data with strategies such as variational inference and maximum likelihood estimation. It’s a learned distribution used to generate new samples. 

After training, these models can create images, texts, audio, and a vast range of data. The best part is they provide variety and can produce creative outputs. 

Applications

Generative models are in various industries. Some of its applications are as follows. 

• Image generation
• Text generation
• Speech synthesis
• Data augmentation

Discriminative Models

They are designed to discriminate beyond various classes and categories of data. They learn the decision boundary present between different classes. But how do they make it possible?

It’s possible because they do it based on input features. 

Key Characteristics

They discriminate with the help of decision boundaries. A hyperplane typically shows it for binary classification tasks. These models majorly focus on learning features that are easy to distinguish. These are learned directly from input data. 

Applications

Below are some domains where discriminative models are an everyday use case. 

• Image classification
• Sentiment analysis
• Named entity recognition
• Regression analysis

Future Of Large Language Models

The future of the large language model is still unclear. But one thing is certain: these models will significantly help businesses. If you think the next generation of LLMs will only be about artificial intelligence or sentiments, then that’s not true. 

Instead, they’ll keep improving and become much more intelligent. These applications will expand in so many ways when it comes to business. The technical expertise will increase and become broad. 

In the future, the training will be on even larger data sets. But the LLMs won’t stop there. 

Instead, it’ll filter all the data for maximum accuracy. It also reduces the chances of potential bias. It’ll do much better content generation in the future than it does now. 

Ending Thoughts

With the advancement in large language models, it’s more important than ever to be quick. You’ve to evolve rapidly and utilize LLMs to their fullest potential. 

It’s transforming various aspects, most specifically interactions between humans and computers. 

LLMs = Transformative leap in artificial intelligence

Many language-related tasks take time and energy. But you can complete them within minutes. How’s that possible?

It’s all because of LLMs. 

Even the way customers interact with LLMs is changing. From chatbots to translations, summarization to text generation, all this is possible because of this model. 

But we can’t deny that there might be some ethical concerns. That’s why the best way to have a balanced approach. Only then you’ll be able to grow your business using this advanced technol