Study and Comparative Analysis of ChatGPT, GPT and DAll-E2

Natural Language Processing (NLP) has evolved greatly over the years, resulting in the introduction of sophisticated models such as GPT, ChatGPT, and Dall-E2 that employ deep learning approaches to evaluate and create humanlike replies to textual material. As a result, they have become indispensable tool for a variety of applications such as language translation, question answering, and text condensation. This research paper compares the aforementioned models. Secondly, the configuration of each model is explained, stressing its particular elements and discrepancies. Second, the study examines the training data which is further used for training the models, examining the dimensions and quality of the data. Lastly, the performance of each model is compared based on its capacity to create reactions similar to those of people. According to the findings, the ChatGPT, GPT, and Dall-E2 models have various strengths and drawbacks when it comes to creating human-like answers to text inputs. Yet, given the enormous quantity of conversational data required to train it, ChatGPT excels GPT and Dall-E2 in providing natural and realistic replies. Furthermore, ChatGPT's design is optimized for dialogue production, giving it an advantage over competing models.