LLMs are deep learning models with billions of parameters that excel at a wide range of natural language processing tasks. They can perform tasks like translation, sentiment analysis, and chatbot conversations without being specifically trained for them. LLMs can be used without fine-tuning by employing "prompting" techniques, where a question is presented as a text prompt with examples of similar problems and solutions.
LLMs typically consist of multiple layers of neural networks, feedforward layers, embedding layers, and attention layers. These layers work together to process input text and generate output predictions.
While LLMs have the potential to revolutionize various industries, it is important to be aware of their limitations and ethical implications. Businesses and workers should carefully consider the trade-offs and risks associated with using LLMs, and developers should continue refining these models to minimize biases and improve their usefulness in different applications. Throughout the course, we will address certain limitations and offer potential solutions to overcome them.