Classification of Generative AI Models

The transformer model has revolutionized the field of natural language processing (NLP) by replacing traditional recurrent neural networks (RNNs) with a self-attention mechanism. This model has achieved state-of-the-art performance on various language tasks while being computationally efficient and highly parallelizable. The core component of the transformer model is the self-attention mechanism, which allows the model to focus on different parts of the input sequence simultaneously when making predictions. Unlike RNNs that process sequential information step by step, the transformer considers the entire input sequence at once, effectively capturing dependencies between tokens. Transformer architecture consists of an encoder and a decoder, both comprising multiple layers of self-attention and feed-forward neural networks. The encoder processes the input sequence, while the decoder generates the output sequence. The self-attention mechanism in the transformer enables the model to selectively attend to relevant parts of the input sequence, facilitating the capture of long-range dependencies and improving translation quality, among other tasks.

The attention module in the transformer adopts a multi-head design. The self-attention is formulated as a scaled dot-product, where the input queries (Q), keys (K), and values (V) are combined to calculate the attention weights. The scaling factor of √d_k is applied to normalize the dot-product scores. The resulting attention weights are then multiplied with the values and summed up to produce the final output.

Attention(Q, K, V) = softmax(QK^T / √d_k)V

The transformer model employs multiple layers of self-attention and fully connected point-wise layers in both the encoder and decoder components illustrated in Figure 10. This architecture allows the model to effectively capture and process the complex relationships and dependencies within the input and output sequences.

Figure 10. Transformer architecture.

Transformers vary in their architectures, specific network designs, and training objectives depending on the application and input data.

BERT (Bidirectional Encoder Representations from Transformers): BERT consists of a multi-layer bidirectional transformer encoder. It employs a masked language modeling (MLM) objective during pre-training. It randomly masks words in the input text and trains the model to predict the masked words based on their context. BERT also uses a next sentence prediction (NSP) task, where it learns to predict if two sentences are consecutive in each document. BERT is pre-trained on a large corpus of text, such as Wikipedia and Book Corpus. It utilizes unsupervised learning and large-scale transformer architectures to capture general language representations. After pre-training, BERT can be fine-tuned on specific downstream tasks using supervised learning with task-specific datasets.

GPT (Generative Pre-trained Transformer): GPT employs a multi-layer transformer decoder. GPT is trained using an autoregressive language modeling objective. It predicts the next word in a sequence based on the previous context, enabling the generation of fluent and contextually relevant text. GPT is pre-trained on a large corpus of text, such as web pages and books. It learns to generate text by conditioning on the preceding context. Fine-tuning of GPT can be performed on specific tasks by providing task-specific prompts or additional training data.

T5 (Text-to-Text Transfer Transformer): T5 employs a transformer architecture like BERT but follows a text-to-text framework. It can handle various NLP tasks using a unified approach. T5 is trained using a text-to-text format, where both input and output are text strings. It leverages a combination of unsupervised and supervised learning objectives for pre-training and fine-tuning.

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) 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), CodeBERT, CODEGEN, and CodeT5. 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) 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), 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) 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 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) 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 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 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, text-to-text transfer transformer (T5), TFix, w2v-BERT (Word2Vec and BERT), and WT5 (Why, T5?). 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 Word2Vec 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. The primary objective of WT5 is to enhance the interpretability and explainability of the T5 model by providing insights into the reasoning behind its predictions.