Large Language Models (LLMs) Explained in Simple Terms!

[Music] Hi everyone, welcome back to the channel. Today we are diving into one of the most popular technologies of our time that is large language models which is also known as LLMs. By the end of this video you'll have a clear understanding of what LLMs are and why they are important. So without the further ado let's get started. So what are LLMs? A large language model or LLM is a type of advanced artificial intelligence system. LLM is a computer program trained on vast amount of data. Hence, it is named as large. It learns patterns, structure and meaning of a language. Therefore, it generate humanlike responses. LLMs are built on a type of neural network called transformer model. LLMs can perform translation, summarization, coding, and much more. The examples of LLMs are chatyp, Gemini, Llama, Cloudy and so on. Now let's see how LLMs work. Actually LLMs are built on type of neural networks called transformer architecture. So here are the simple process of how LLM works. The first is collection of huge amount of text data. That means LLMs are trained on huge amount of text data either from books, articles, websites and so on. Next step is data cleaning. So before training the model, first there is a cleaning of data such as removing duplicates, lowquality content, wrong data and so on. Another is tokenization. In this process, texts are splitted into smaller units called tokens. For example, I love coding will be splitted like I love coding. Next step is embedding which means each token is converted into a numerical vector. Attention mechanism. This is very important part of using transformers. Here the model look to all the sentences, words or figures and pays attention to the most important part of a sentence. For example, in a sentence, a boy fell asleep because he was tired. Here he refers to a boy. So this is the attention mechanism. Next is training on data. During the training, the model process billions and trillions of data predicting next word and if it is wrong, the model corrects its knowledge by referring its huge data. And this process is repeated until the model learns proper patterns, creativity, and reasoning. The next step is fine-tuning with human feedback. That means after the training process, humans give feedback. So it learns to give safer and more better answer. The last step is inference. Once our model is trained, we can give prompt to the model and then it generate our response by predicting words one by one. For example, Tokyo is in then it will predict Japan. What are LLMs used for? LLMs are used in chatbots and virtual assistants. They are used for answering customer queries, performing back-end task and providing various information to users as a part of customer services. LLM can generate code according to user instructions and can also perform debugging if required. These days, some LLMs can perform sentiment analysis to help users to find customers intention or a response. LLMs are used for language translation. If LLMs are trained on various languages, it can break the barrier among multilingual environment. LLMs are used for summarizing multiple pages of articles, news, research papers and so on. Here are some of popular LLMs recently. Open AI GPD family such as GPT 3.5, GPT4, Google's Deep Mind Gemini which excels at long context reasoning, Meta's open-source model which are widely used by researchers, Anthropics Cloudy which is known for safety focused design. Google and Facebook's Bird and Robot are good at embedding and understanding task. Mistral which is popular in open-source communities. Therefore, they are some of the popular LLMs recently and each LLM has its own strength and benefits. Advantages of LLM. Scalability. LLMs can handle enormous amounts of data. Continuous improvements. LLMs can learn and adopt new data continuously and improve the performance as they are fine-tuned. Flexibility. LLMs are versatile. That is one LLM can be used for different tasks such as translation, summarization, text generation and so on. Efficiency LLMs make our life easier as they automate our routine task. Easy on training. Many LLMs are trained on unle data. So it is less time consuming. Hence these are some of the advantages of LLM. Now let's see the disadvantages. Disadvantages of LLMs. Despite many benefits of LLMs, they have some limitations too. Data privacy risk. LLMs process huge amounts of data. If there are some sensitive data, the model should ensure its confidentiality. Hallucinations. Sometimes LLM can provide false information with confidence. So it may cause problem bias. Since LLMs are trained by humans from many sources, they may reflect biases in those data. High operating cost. Cost of operating LLM can be high as they are trained on large amount of data and can require expensive computing power. Ethical concerns. LLMs may have issues on data plagurism or malicious purposes. Why LLM are important? LLMs make knowledge easily readable and accessible than earlier days by summarizing long and complex topics. They allow people to use natural language instead of complicated commands. With LLMs, we have powerful tools such as chatboards, AI assistance and much more. LLMs allow computers to interact in more natural way with humans. Therefore, in this way, LLMs are very important to us as they bring technology more closer. Okay, that's all for today. We understand LLMs are AI systems designed to understand human language and they work using transformer neural networks and large amount of data. They are important because they are one of the most revolutionary technologies of our era. I hope you like this video. 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