A Gentle Introduction to Large Language Models

This article, “A Gentle Introduction to Large Language Models“, uncovers the high level science and intuition behind the very popular ‘Large Language Models’ along with their key real-world applications.

This article covers the following key topics:

1. What are Large Language Models?
2. How Large Language Models work?
3. Common Applications of Large Language Models
4. Conclusion

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Let’s learn about these topics in more details.

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1. What are Large Language Models?

Large Language Models, or LLMs for short, are advanced AI models designed to understand and generate human-like text (hence the name language model). LLMs are trained to handle and produce written content in a way that feels natural and coherent.

One can think of LLMs as incredibly smart text-based assistants. They are trained on vast amounts of written language materials such as books, articles, crawled data from websites, research papers and so on.., and try to learn how the language works.

This extensive training helps them in grasping the nuances of grammar, context, and meaning of the desired language. It further allows them to generate responses that are relevant and fluid.

For instance, when you ask an LLM a question, it uses its training to craft an answer that makes sense and fits the context of your query. Similarly, if you need help in drafting an email or brainstorming ideas, LLMs can offer suggestions that sound natural, smart and appropriate.

In essence, LLMs are designed to handle various language tasks by leveraging their deep understanding of how words and sentences come together, making them valuable tools for communication and content creation.

In the next section, we will learn – How a large model actually works!

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2. How Large Language Models work?

LLMs, or Large Language Models, have complex design and are not trivial to train. It is difficult to understand every small details about them in a single blog post. In this section, we will look at them on a high level and understand their key concepts.

At their core, LLMs are like super-powered text generators that use advanced math and huge amounts of data to understand and produce human-like text. Let’s walk through the key aspects of how they operate.

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Let’s break this section into the following four key aspects of LLMs, for easier understanding:

1. Structure
2. Training
3. Predictions
4. Scale

Let’s look at them one by one.

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1. Structure

Think of LLMs as a giant, multi-layered neural network. In most cases, this giant neural network follows the popular Transformer like building blocks. Let’s get some more details about the structure of these networks:

• Attention Mechanism: The Transformer uses something called “attention“. Think of it as a weighting mechanism applied on different parts of a sentence. It figures out which words are more important in relation to each other and puts a spotlight (more weight) on them. For example, in the following sentence “The cat sat on the mat,” the ‘cat’ is more closely related to ‘sat’, than it is to ‘mat’, and the attention mechanism can help you understand such relations.
  
• Network Layers: The Transformer architecture is consists of several layers, each with multiple “attention heads“. Each head looks at different aspects of the sentence and helps the model in understanding the text from various angles. Due to multiple heads, this attention mechanism is also termed as “multi-head attention”, or MHA. Such layers are usually stacked on top of each other to help the model learn more complex patterns from the training data.
  
• Positional Encoding: Since the Transformer doesn’t inherently know the order of words in the input sentence(unlike some other models), it uses something called positional encoding. Using positional encoding, we can let the model know about position (or sequential order) of each word (or token) in an input sentence. As we know, the order of words matters in the sentences, this is an important step to incorporate it into the model.

Now that we know about key building blocks of LLMs, let’s learn about their training next.

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2. Training

Just like any other AI model, LLMs need training so that they can learn to read and write the language text. Training of LLMs basically means the following two aspects:

Pre-training phase

During the pre-training phase, the model learns from a huge collection of textual data including books, articles, websites and so on. During the training, the model tries to guess the next word (or token) in a sentence based on the words that came before it.

For example, if the input is “Rohan is drinking a glass of ______“, the model learns to predict “water“. This kind of training helps the model get a basic understanding of the language patterns, grammar, and some general knowledge.

Due to the large size of the model and huge training corpus, even this simple training trick makes the LLMs an expert on the language, and in turn capable of answering general questions, and generating content for a desired topic.

Fine-Tuning phase

After the pre-training is complete, we can adjust the model for specific tasks. For example, if we want the model to answer questions, we fine-tune it on a dataset of questions and answers. This gives the model an idea on how to perform a given task by learning from examples that are more focused. LLMs can be fine-tuned for a number of different tasks such as text-summarization, question-answering, writing code and so on.

Once the training and (optionally) fine-tuning is complete, we can start making predictions. Let’s learn more about predictions in next subsection.

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3. Making Predictions

As discussed, LLMs are text generators. In order to get predictions from them, we need to pass some input text (context), as input. This input text is termed as “prompt“. LLMs use this prompt as context and generate content from it.

A prompt can be a question, a statement, a command or any form of text that guides the model to generate content based on the context provided.

When we give an LLM a prompt, here’s what happens:

1. Tokenization: As a first step, the model breaks down the input text (or prompt) into smaller pieces called tokens. These tokens are sometimes unique words or even parts of words (a word can be represented with one or more tokens). Tokens basically convert the input text into a numerical sequence as the models only understand numbers. Tokenization helps the model process the text in manageable chunks.
   
2. Context Understanding: The model uses its knowledge (gained during training phase) to understand the context of the input. The model then processes each token considering how they are related to each other (as learned during the training).
   
3. Generating Output: Finally, the model starts produces output content one token at a time (similar to training phase, next token is predicted based on the previously passed tokens). The model makes predictions about what should come next, based on the context provided. Similarly, it keeps generating the subsequent tokens (one at a time), to come up with the complete output. There are different settings that control the generated text, we can choose to be more creative with the content while staying relevant to the prompt. These numerical output tokens are then converted back to text (using a mapping dictionary) and returned as output.

We have now understood how LLMs generate content. Next, let’s learn how we can handle these large scale models.

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4. Handling the Scale

LLMs are often very large and powerful, all thanks to the billions of parameters they have. During the training phase, these parameters are adjusted in such a way that the model produces desired outputs.

Training the LLMs, requires a lot of computational power, often large clusters of powerful computers (with accelerators such as GPUs and TPUs) working together. Due to the large training set and billions of model parameters, the training process is quite time consuming and costly. It is not feasible to perform pre-training very often. Most of the time, fine-tuning does a decent job.

Let’s now look at some of the common applications of LLMs.

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3. Common Applications of Large Language Models

Large Language Models, or LLMs for short, have tons of applications and that’s the reason they are often developed despite the high cost of training, time, and computational resources.

Following are some key applications of LLMs:

• Chatbots: Due to their ability to answer questions, LLMs are often used as customer support assistants on websites and apps. LLMs are capable of chatting with users (providing human-like experience), provide useful information and even help with troubleshooting.
  
• Writing Assistance: LLMs are really good at writing and can work as a writing assistant. They are capable of generating text, suggesting improvements, generating stories, creating reports and so on, based on the provided prompts.
  
• Language Translation: LLMs are often trained on multiple languages. They can be utilized for translating text from one language to another, making it easier to understand and communicate in various different languages. The support of multiple languages makes them useful for people all around the world.
  
• Summarization: Another common application of LLMs is text summarization. They are capable of reading log articles or paragraphs and providing a concise summary for them. This summary highlights the main points of the large text while ignoring the extra details, hence saving time for the users.
  
• Content Creation: The capability of generating creative content is quite useful for content creators. LLMs can generate creative content such as poetry, movie scripts, songs and so on, based on the specific themes or ideas provided through the prompt. Hence, they can be a good buddy for content creators.
  
• Personal Assistants: Considering the capabilities of LLMs, they make a really cool: virtual personal assistants. They can help with tasks such as setting reminders, answering questions and finding information online.
  
• Educational Tools: LLMs can provide simple explanations of complex concepts, help with the homework, or offer tutoring in various subjects. Thus they can be very helpful for educational purposes.
  
• Coding: LLMs can assist with writing or debugging code, explaining programming concepts, and suggesting improvements. They are really good at formatting already written code and adding documentation/comments as instructed.

These applications show that LLMs can be quite useful in everyday tasks, making complex processes easier by understanding and generating human-like text. We have only discussed some common applications but in reality the possibilities and their applications are endless.

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4. Conclusion

In this article, we discussed about some key concepts related to the Large Language Models (LLMs). Specifically, we learned the following concepts on a high level:

1. What are LLMs and how do they work?
2. We learned about key pieces related to their structures, a high level overview of training and how to make predictions with trained models.
3. Finally, we looked at the key applications of LLMs

I hope this article, A Gentle Introduction to Large Language Models, was helpful to the readers in understanding LLMs. Please let me know your thoughts by commenting below.

See you in the next article!

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