## 3 Results
### Classification of Generative AI Models
#### 3.2.6 Transformers
The field of transformers has witnessed remarkable progress, leading to the development of several influential models for various natural language processing (NLP) tasks. One prominent model is the adaptive text-to-speech (AdaSpeech) [113] system, which focuses on generating highly realistic and natural-sounding synthesized speech. It employs advanced techniques to overcome limitations in traditional text-to-speech systems, enabling more expressive and dynamic speech synthesis. For code-related tasks, researchers have introduced specialized transformer models such as code understanding BERT (CuBERT) [110], CodeBERT [114], CODEGEN [50], and CodeT5 [115]. CuBERT is specifically designed for code comprehension, leveraging the power of transformers to understand and analyze source code. CodeBERT, on the other hand, performs code-related tasks like code generation, bug detection, and code summarization. CODEGEN focuses on generating code snippets given natural language descriptions, facilitating the automation of programming tasks. CodeT5, inspired by the T5 architecture, excels in various code-related tasks, including code summarization, translation, and generation. The feed-forward transformer (FFT) [42] model is a versatile transformer architecture that has demonstrated exceptional performance across multiple NLP tasks. It leverages a feed-forward neural network to process and transform input sequences, enabling effective modeling of complex language patterns and semantic relationships. The GPT language model (Codex) [116], based on the GPT-3 architecture, has gained significant attention for its ability to generate coherent and contextually relevant text. It excels in tasks such as text completion, question answering, and text generation. InstructGPT (GPT-3) [111] is another powerful language model that can understand and generate human-like text based on specific prompts. It has been extensively used in various conversational AI applications, virtual assistants, and creative writing assistance. Grapher [117] is a transformer model designed to process and understand graphical data. It leverages graph neural networks and self-attention mechanisms to capture dependencies and relationships within structured data, enabling tasks such as graph classification, node-level prediction, and link prediction. Language models for dialog applications (LaMDA) [33] are transformer-based models specifically tailored for conversational tasks. They enhance dialogue understanding and generation by capturing context, nuances, and conversational dynamics. LaMDA models have shown promise in improving conversational agents, chatbots, and virtual assistants. In the realm of multimodal tasks that involve both text and visual information, transformer-based models have also made significant contributions. MotionCLIP [118] focuses on understanding and generating textual descriptions of videos, bridging the gap between language and visual understanding. Muse explores the connection between text and image, enabling tasks such as text-based image retrieval and image captioning. The pre-trained language model (PLM)/visual GPT [65] is a multimodal model that combines text and visual information to generate coherent and contextually relevant captions for images. Other notable transformer models include T5X [119], text-to-text transfer transformer (T5) [39], TFix [45], w2v-BERT (Word2 Vec and BERT) [120], and WT5 (Why, T57) [121]. T5X extends the T5 architecture to handle even more complex NLP tasks and demonstrates superior performance in tasks such as machine translation and text summarization. TFix focuses on addressing issues related to fairness, transparency, and explainability in transformer models. w2v-BERT combines Word2 Vec and BERT to enhance the representation of word semantics within the transformer framework. WT5 focuses on training text-to-text models to explain their predictions. It builds upon the architecture of the text-to-text transfer transformer (T5) model.