What is the Large Language Model (LLM)? In which fields is it used?

If you are reading this content, you have likely heard some information about large language models (LLMs) beforehand. A quite popular topic. The reason is that LLMs are behind popular tools fueling the generative AI revolution, such as ChatGPT, Google Bard, and DALL-E...

To present the magic of these tools, they rely on powerful technology that processes data and generates accurate content in response to user-asked questions. This is where LLMs come into play.

In this content, we aimed to introduce you to LLMs. We explained what LLMs are, how they work, different types of LLMs with examples, and their advantages.

If you're ready, let's get started! 🤓

What is a Large Language Model (LLM)?

Large language models, or LLMs, are artificial intelligence models based on deep learning that use transformer models to understand and generate text.

Language translation, text classification, sentiment analysis, text generation, and question answering are some of the natural language processing (NLP) tasks they help accomplish.

In short, we can say that large language models (LLMs) are artificial intelligence models used to model and process human language. They are called "large" because these types of models consist of hundreds of millions to billions of parameters that define the model's behavior.

LLMs are trained with large datasets sourced from various places. These datasets can reach tremendous sizes. Some of the most successful LLMs have hundreds of billions of parameters.

These parameters significantly affect the model's grammar, logic, and knowledge acquisition capabilities. For example, GPT-3 was trained with approximately 175 billion parameters. Its competitor, LLaMA 2 was trained with 70 billion parameters.

Origins of Large Language Models

The technology underlying LLMs is called transformer neural networks. The transformer is an innovative neural architecture in the deep learning field.

As presented in the famous paper titled “Attention is All You Need” by Google researchers in 2017, transformers can rapidly perform natural language (NLP) tasks. In fact, without transformers, the current generative artificial intelligence revolution would not be possible.

Language is the foundation of human interaction; it helps us convey ideas, build relationships, and manage the complexities of our social and professional lives. Beyond being a means of communication, language is the environment through which we access the world.

As technology advances, our interaction with tools and technologies has increasingly relied on natural language, making our communications with machines more intuitive and meaningful.

You can see this development in the graph above. As you can see, the first modern LLMs were created immediately after the development of transformers.

Important examples include Google's BERT, the first LLM developed to test the power of transformers, and the first two models of OpenAI's GPT series, GPT-1 and GPT-2. However, LLMs only became mainstream in the 2020s, becoming increasingly larger and therefore more powerful.

💡 What is Co-LLM?

MIT researchers developed the "Co-LLM" algorithm to enhance the collaboration of large language models (LLMs).

This algorithm allows a general-purpose LLM to collaborate with a specialist LLM on more complex topics, allowing for increased accuracy and efficiency.

The algorithm uses a "variable" to determine when the specialist model needs assistance, seeking support from the specialist model for tasks in specialized areas such as medical questions or mathematics. This approach mimics human teamwork. For more information, you can click here.

What Are the Types of Large Language Models (LLMs)?

As the application areas of large language models (LLMs) have expanded, different types have emerged to meet specific needs and challenges. The main categories of LLMs are:

1. Task-Oriented LLMs

These models are fine-tuned LLMs for specific tasks such as summarization, translation, or question answering. Since they focus on a specific function, they can offer higher performance and efficiency in these tasks.

2. General-Purpose LLMs

These models are designed to perform a wide range of language tasks without any specific training. They can generate complex texts, understand context, and answer questions on various topics. Their versatility makes them suitable for a wide range of applications.

3. Domain-Focused LLMs

Law, medicine, or finance are developed to provide expertise in specific fields. These LLMs are trained with specialized datasets, enabling them to understand and generate content specific to their domains with higher accuracy.

4. Multilingual LLMs

Considering global communication, multilingual LLMs are developed to understand and generate text in multiple languages. These models are essential for creating AI systems that can serve different communities and help overcome language barriers in accessing information.

5. Few-Shot Learning LLMs (Few-shot learning)

These models can perform tasks with minimal examples or guidance. Their ability to quickly adapt to new tasks provides flexibility and efficiency in applications where extensive training data is not available.

How Does a Large Language Model (LLM) Work?

LLMs work by leveraging deep learning techniques and large amounts of data. These models typically rely on a pre-trained transformer architecture that excels in processing sequential data like text input. In other words, the key to the success of LLMs lies in this transformer architecture.

LLMs consist of multiple neural network layers, further enhanced by numerous layers that each have parameters that can be adjusted during training.

During training, these models learn to predict the next word in a sentence based on the context provided by the previous words. The model does this by assigning a probability score to the recurrence of words. 

To ensure accuracy, this process involves training the LLM on a massive dataset (in billions of pages). It allows the model to learn grammar, semantics, and conceptual relationships through self-supervised learning. 

After being trained on this training data, LLMs autonomously predict the next word based on the input. They can also generate text by using the patterns and information they have learned. 

Of course, some undesirable situations can occur during this process. The output provided by the model may not deliver the desired performance, and there may be biases, hate speech, and responses referred to as "hallucinations" which are unrealistic answers. To mitigate these, methods such as Reinforcement Learning from Human Feedback (RLHF), prompt engineering, prompt tuning, and fine-tuning can be used. 

What Are the Use Cases of Large Language Models (LLMs)?

We can list the following as example use cases for LLMs;

1. Chatbots and Virtual Assistants: LLMs are used in chatbots to help with customer support, lead tracking, and personal assistance.
2. Code Generation and Debugging: They assist programmers by generating code snippets and identifying and correcting errors in code.
3. Sentiment Analysis: Sentiment analysis can be performed with LLMs. They can automatically understand the sentiment of a text piece to ensure automatic comprehension of customer satisfaction.
4. Text Classification and Clustering: They can organize, categorize, and sort large amounts of data to identify common themes and trends.
5. Translation: LLMs can translate documents and web pages into different languages. For example, Meta's SeamlessM4T model can perform speech-to-text, speech-to-speech, text-to-speech, and text-to-text translations for up to 100 languages.
6. Summarization: They can summarize articles, writings, customer requests, or meeting notes and highlight the most important points.
7. Content Generation: LLMs can develop a draft or create new content that can serve as a good first draft to build upon.
8. Autocomplete: LLMs can be used for autocomplete tasks in emails or messaging services. For example, Google's BERT supports the autocomplete tool in Gmail.

What Are the Advantages of Large Language Models (LLMs)?

LLMs are already used in many areas. We can look at ChatGPT to see this. ChatGPT became the fastest-growing digital application of all time just a few months after its release.

Below, we have listed some advantages of LLMs:

• Content Creation. LLMs are ideal tools for generating content (mostly text, but can also generate images, videos, and audio in conjunction with other models). They can provide domain-specific content in every industry you can think of, from law and finance to software and marketing.
• NLP Tasks. As explained in previous sections, LLMs perform well in many NLP tasks. They can understand human language and interact with humans. However, we would also like to remind you that these tools are not perfect and can still produce incorrect results or hallucinations.
• Increased Efficiency. One of the key benefits of LLMs is their ability to help complete time-consuming tasks within seconds.
• Zero-Shot Learning. LLMs can perform tasks they were not explicitly trained on (this is known as zero-shot learning). This means they can understand and execute instructions in contexts they have never encountered during training, demonstrating a groundbreaking level of adaptability and comprehension in artificial intelligence.
• Use of Large Quantities of Data. The massive scale of LLMs allows them to process and analyze large datasets that exceed human capacity. This enables the discovery of hidden patterns, insights, and relationships within the data. This capability is invaluable for all fields based on research, business intelligence, and large-scale data analysis.
• Ability to Automate Various Language-Related Tasks. From writing and summarizing text to translating and customer service, LLMs can automate a wide range of activities. This automation can significantly reduce the time and resources required for specific functions, allowing human workers to focus on more creative and complex challenges.

Why Have Large Language Models (LLMs) Suddenly Started to Become Popular?

Recently, there have been many technological advancements that have brought LLMs to the forefront:

1. Advancements in Machine Learning Technologies

   • LLMs benefit from many developments in ML techniques. The most noteworthy is the transformer architecture, which underlies most LLM models.

2. Increased Accessibility

   • The release of ChatGPT opened the door for anyone with internet access to interact with one of the most advanced LLMs through a simple web interface. 

3. Increased Computational Power

   • The availability of more powerful computing resources such as Graphics Processing Units (GPUs) and better data processing techniques has enabled researchers to train much larger models.

4. Quantity and Quality of Training Data

   • The availability of large datasets and the ability to process them significantly improved model performance. For example, GPT-3 was trained on large datasets that include high-quality subsets like the WebText2 dataset (17 million documents).

Examples of Popular Large Language Models

Today, the number of open-source LLMs is rapidly increasing. You might have heard of ChatGPT, but ChatGPT is not an LLM; it is an application built on top of an LLM. Other popular LLM models include:

1. PaLM

Google's Pathways Language Model (PaLM) is a transformer-based language model. It supports Google Bard, the most ambitious chatbot designed to compete with ChatGPT. 🌴

2. BERT

Bidirectional Encoder Representations from Transformers (BERT) is one of the first modern LLMs developed by Google. It is a transformer-based model that can understand natural language and answer questions. 

3. LLaMa 2

Developed by Meta, LLaMa 2 is one of the most powerful open-source LLMs on the market. 🦙

Understanding the Basics of LLM

Language Models (LLMs) have revolutionized the field of Natural Language Processing (NLP) by enabling machines to understand and generate human-like text. In this article, we discussed what LLMs are and how they work. 

Before diving into advanced topics in the field of LLM, it is crucial to thoroughly understand the basic concepts. If you are interested in areas like artificial intelligence, deep learning, and machine learning, you can check out our bootcamps, and learn the fundamentals of LLM.