How does Generative AI work?
Two essential components for building Generative AI: Supervised Learning and LLMs (Large Language Models)
Generative AI involves creating models that can generate new content, such as text, images, or music. A fundamental strategy in training these models is the utilization of Supervised Learning, a process where the model acquires insights from labeled examples comprising input-output pairs (A -> B). These pairs serve as instructional cues, guiding the model to understand relationships and patterns within the data. For instance, in text generation, the model learns how words and phrases correlate, while in image generation, it discerns visual features and structures. This training method enables the model to generalize its understanding and generate novel, contextually relevant content based on the learned associations.
Supervised Learning for LLMs:
When we train Large Language Models (LLMs), we give them lots of sentences to learn from. Each sentence has a bunch of words in a specific order. The model's job is to figure out what word comes next in the sentence.
Imagine it's like teaching the model to complete a sentence. If we show it the sentence "The sun is shining," it learns that after "shining," words like "brightly" or "today" might come next. This way, it learns the patterns of how words usually follow each other in sentences.
By doing this with many examples, the model gets really good at predicting the next word based on the words that came before. It's like teaching it the rhythm and flow of language, so when you ask it to generate new sentences, it can do so in a way that sounds natural and makes sense.
Building Large Language Models (LLMs):
Large Language Models are designed to understand and make up sentences that sound just like something a human would say. These models are packed with lots of settings and are trained on tons of text from books, articles, and websites. During training, they learn all about how words fit together, like learning a new language by reading lots of books. They figure out things like grammar, context, and what words usually come next in a sentence. These models are not just good at understanding language; they're also really versatile. They can help write stories, complete sentences, or even translate languages.
Repeating Predictions:
LLMs are trained to predict the next word in a sequence repeatedly. This process is iterative, and the model refines its predictions over time as it processes more examples. The goal is to enhance the model's ability to generate coherent and contextually relevant sequences of text.
Fine-Tuning:
After the initial pretraining on a large dataset, LLMs can undergo further training on smaller, task-specific datasets through transfer learning and fine-tuning. This helps adapt the model to more specialized contexts or tasks.
In summary, the core idea is that Large Language Models, powered by Supervised Learning, learn from extensive datasets to predict the next word in a sequence. This ability to capture language patterns and generate coherent text makes them valuable tools in various natural language processing applications.