What is LLM (Large Language Model) | How Large Language Models Work?

What is LLM (Large Language Model) | How Large Language Models Work? | Edureka

Imagine you have a brilliant friend named Max who has read every book, article, and social media post on the internet. Max can recall entire conversations, understand details of language, and generate responses that are both informative and engaging. One day, you request Max to help you write an email to a colleague. Max suggests different words, fixes any mistakes in spelling or grammar, and even makes the email more engaging to read. Next, you get help to summarize a lengthy report on a technical topic. Max simplifies the main points into a clear and concise summary, saving you hours of reading time. Later, you engage in a conversation with Max about a complex topic like artificial intelligence. Max responds thoughtfully, using its vast knowledge and understanding of language to provide insightful answers and ask follow-up questions that stimulate further discussion.

So, this is what large language models like Max can do: process and generate human-like language, understand context and meaning, and assist with various tasks. On that note, hello everyone, and welcome to this video on what is large language model by Edureka. In this video, we will discuss some of the examples of large language models and their applications, followed by the working of large language models.

Before we begin, please consider subscribing to our YouTube channel and hit the Bell icon to stay updated on the latest content from Edureka. Also, visit the Edureka website for our large language models course with generative AI to dive deep into the LLMs and acquire proficiency in content generation and application development. The course link is in the description box below.

Moving on to some examples of large language models, such as Google's BERT, Meta's LLaMA, OpenAI's GPT-3, and Microsoft's Turing NLG. These models have many applications, such as language translation for accurately translating text between different languages, text summarization for understanding the meaning and context of the text for tasks like sentiment analysis, question answering, and summarization, chatbots and virtual assistants, and content generation for creating human-like text for content creation, chatbots, and storytelling. Additionally, it is used for content recommendations, search engine sentiment analysis, and data analysis.

Large language models work by using a combination of natural language processing (NLP) and machine learning algorithms to process and generate human-like language. So now, moving ahead, let's have a look at how large language models work.

First, the model is trained on a massive dataset of text, such as books, articles, and websites. This dataset is used to learn patterns and relationships in language. The text is then broken down into individual words or tokens, which are used as input for the model. Each token is converted into a numerical representation called an embedding, which captures its meaning and context. The embeddings are fed into an encoder, which uses a series of transformer layers to analyze the input text and generate a contextualized representation. Then, the decoder generates output text one token at a time based on the contextualized representation and the model's understanding of language patterns. The model can generate text in various forms, such as continuation of prompt, text completion, or even entirely new text. The model can be fine-tuned for specific tasks, such as language translation or text summarization, by adjusting the weights and biases of the neural network.

I hope the working of large language models is clear to you. But to help you understand better, let me explain with a simple example. Think of a large language model like a smart parrot named Polly. Polly lives in a busy town where she learns to talk by listening to people chatting. She picks up words and phrases from their conversations, just like we learn from hearing others speak. Polly's training begins when she listens to the people around her chatting, telling stories, and sharing news. She pays close attention to the words they use and how they are put together. Just like Polly listens for individual words and phrases, the text is broken down into small chunks called tokens. Each token represents a word or part of a word, making it easier for Polly to understand.

Polly not only learns words but also understands their meaning and how they are used in different contexts. She associates words with their meanings, just like we do with pictures. When Polly hears a conversation, she processes the words she hears and makes sense of them using her knowledge of the language. She can understand the flow of the conversation and respond appropriately. Polly's responses are like pieces of a puzzle that fit together to form a meaningful conversation. She uses her understanding of language patterns to generate responses that make sense in the context of the conversation.

Polly can generate responses based on what she has learned from listening to conversations, whether it's answering questions, sharing information, or telling stories. Polly can speak fluently like a human. Just as Polly learns to mimic different words, phrases, and accents by listening to other people, the language model can be fine-tuned for specific tasks like translating between languages or summarizing text.

With this, we have come to the end of this video on what is a large language model. I hope you enjoyed the video, and if you did, make sure to like and subscribe to our YouTube channel. Thanks for watching and happy learning. I hope you have enjoyed listening to this video. Please be kind enough to like it, and you can comment on any of your doubts and queries, and we will reply to them at the earliest. Do look out for more videos in our playlist and subscribe to the Edureka channel to learn more. Happy learning!

Keywords

Large language models (LLMs)
Natural Language Processing (NLP)
Machine learning algorithms
Text summarization
Language translation
Chatbots
Virtual assistants
Content generation
Embeddings
Transformers
Encoder
Decoder
Contextualized representation

FAQs

Q1: What are some examples of large language models? A1: Some examples of large language models include Google's BERT, Meta's LLaMA, OpenAI's GPT-3, and Microsoft's Turing NLG.

Q2: How are large language models trained? A2: Large language models are trained on massive datasets of text, such as books, articles, and websites, to learn patterns and relationships in language.

Q3: What applications do large language models have? A3: Large language models have applications in language translation, text summarization, chatbots, virtual assistants, content generation, content recommendations, sentiment analysis, and data analysis.

Q4: How do large language models process text? A4: Large language models break down text into tokens, convert these tokens into numerical embeddings, and then use transformers to analyze and contextualize the input, generating output text based on this understanding.

Q5: Can large language models be fine-tuned for specific tasks? A5: Yes, large language models can be fine-tuned for specific tasks like language translation and text summarization by adjusting the weights and biases of the neural network.

Q6: What is the role of embeddings in large language models? A6: Embeddings capture the meaning and context of tokens and are essential for transforming text into a format that the model can analyze and understand.