Akansha Yadav breaks down how text-to-text generation works, from training on massive datasets to transformer models like GPT and T5. The technology powers summarization, chatbots, and email drafting with error rates around 0.01%.
Ladies and gentlemen, hello everyone, welcome back to my YouTube channel. In today's video, we are going to see how text-to-text generation in AI works, that how these generative AI is generating a new text for us, how this text-to-text generation works. So let's get back on the screen and see. So let's see how does text-to-text generation works actually. So text-to-text generation models are utilized using the generative AI techniques to generate the text from the given input, just like we give a prompt to ChatGPT, and it generates the response on the basis of the prompt that we have given. We define the role, we define everything, even the tone, we also define it in the prompt. So as per the prompt that we are giving to the ChatGPT, the ChatGPT generates the new text for us and gives us into the output. It has the training capability that trained on the vast text corpus, and these models grasp the patterns and the grammar and the contextual details. So basically, whatever we have trained to those models, the data that we have given, it has, or it has just grasped the pattern, uh, from that data and it has understood the grammar portion of it, due to which it is giving us the correct grammatical outputs. And even though it is giving us the new generated text, there are different types of text-to-text generation models, out of which statistical model is one of them, which utilizes the statistics techniques such as Markov chain for the text generation. Another type of a model is the neural network model, which employs the artificial neural networks for generating text trained on large text corpora. These models do have some architecture, and these architectures are something like this. Let it can be a sequence-to-sequence model which encodes input text into the sequence of the numbers and then decode them into the generated text. Another type of the architecture is the transformer model which directly maps the input text to generated text and the leverage— it leverage basically the attention of the mechanism for the context. There are multiple popular models I can say, but these are few of them. One is the GPT which I have given you the example in this beginning as well, which is Generative Pre-trained Transformer which is developed by the OpenAI and which is capable of doing various tasks. If you are going the ChatGPT 4 version, you can do other tasks like the data analyst or the data scientist. But if you are using 3.5 version, still you can do like various tasks which includes translation, creative content generation, and many more. Another type is the T5, which is Text-to-Text Transfer Transformer, which is developed by the Google AI, and it serves as a universal model for the diverse NLP task for transferring learning capabilities. Another one is by the Facebook, which is the BART one, which is Bidirectional Auto-Regressive Transformer, which combines the bidirectional encoders and represent augmentation and autoregression and decoder for versatile NLP and the computer vision task. This can perform computer vision tasks, but GPT can't perform computer vision tasks. GPT do not have any capability to analyze your images or the videos that you are going to provide, but Bard can do that. There are multiple applications to use it, just like text summarization, or if you wanted to have a conversation, if you are learning something, you can use these generative AI text-to-text generation models to get the intelligence level of the second person, and product description generation, email writing, resume creation, and many more. Like if you are doing your assignments for the college, you can use this. There are multiple benefits as well, like automating the task to increase the productivity and improve the accuracy in the task, because human can make the mistake, but the models that we have created are trained on tons of data which can like make the mistakes, but it would be 0.01% only. So this is how text-to-text generation generative AI works. I hope you like the video. If you have any questions, you can drop it down into the comment section.
