Define LLMs in Artificial Intelligence? Large Language Models

Last updated on January 26, 2025

LLMs also known as Large Language Models are the fundamental build blocks in Artificial Intelligence. It employs neural network technique with advanced language processing with extensive parameters. We will further cover design pattern, architecture, application and challenges of LLMs and impact its in Natural Language Processing.

What are Large Language Models?

The Large Language Models or LLMs are the systems which apply neural network phenomenon which lots and lots of datasets. The LLMs are used for understanding human languages or text with the help of supervised data processing algorithms.

Photo by Jona on Unsplash

Applications:

1. Text Generations
2. Video Generations
3. Code Generation and analysis of code
4. Chatbots :
5. Examples ChatGPT, Gemini, Co-Pilot

LLMs basically follow deep learning. They are very good in establishing relationships between the conditions provided in solving a problem. They use semantic and syntactic approach for text generations.

There are several GPTs Generative Pre-trained Transformer (LLMs)

1. GPT-3.5
2. GPT-3.5 Turbo
3. GPT-4
4. GPT-4o
5. GPT-4o mini

What is the underlying process behind Large Language Models LLMs?

The LLM’s work on the principle of deep learning and neural networks. These use neural network to understand human languages. The vast amount of data is required to train these LLMs. These are dependent on the sequential tokens which help in finding dependencies and relationships

Pillars of LLMs

LLMs are composed of different layers:

1. Feed forward layers
2. Embedding layers
3. Attention layers

The composition of LLMs is based on objective specific model, computation resources and type of language processing works that needs to be executed.

Components of Large Language Models

1. Size of the model and number of parameters
2. Ways in which inputs can be shown
3. Self-Attention phenomenon
4. Efficiency of computation
5. Decoding and output generation
6. Objectives of training

Popular LLMs

1. GPT-4
2. LLAMA
3. GEMINI
4. Falcon

What is Natural Language Processing?

The natural language processing is a term that focussed on interaction between humans and machines. This is one of the facinating part of artificial intelligence.

What it involves?

Understanding: computers analyse human terminology, interpret the meaning behind them.

Generation: computers can produce human like texts, responses.

Conversion: it can make speech to text and text to speech.

What are the applications?

1. Text analysis: It involves sentiment analysis, summarisation and text categorisation
2. Machine translations: translates text from one language to another language such as Google translator
3. Chatbot an virtual assistants: creating conversational programs that can converse and assist with users
4. Voice activated assistants: devices that respond to commands such as Alexa

Techniques:

1. Deep Learning: using neural network to enhance NLP.
2. Machine Learning: usage of algorithms to understand language and its generation.
3. Syntactic and Semantic Analysis: Understand the structure and the meaning of the sentence

Lets create a simple program that tells the concept of LLMs using python and hugging face transformers:

Output:

In the given program we have made one example of text completion based on the given prompt. The text completion output will be different every time you run the code.

Conclusion:

Large Language Models are transforming the way it interacts with the technology. Their ability to understand and generate human languages is endless. The possibilities are endless and the future looks bright.