Abstract
The emergence of Generative AI (AI) and LLMs (LLMs) has marked a new era of NLP (NLP), introducing unprecedented capabilities that are revolutionizing various domains. This paper explores the current state of these cutting-edge technologies, demonstrating their remarkable advancements and wide-ranging applications. Our paper contributes to providing a holistic perspective on the technical foundations, practical applications, and emerging challenges within the evolving landscape of Generative AI and LLMs. We believe that understanding the generative capabilities of AI systems and the specific context of LLMs is crucial for researchers, practitioners, and policymakers to collaboratively shape the responsible and ethical integration of these technologies into various domains. Furthermore, we identify and address main research gaps, providing valuable insights to guide future research endeavors within the AI research community.
Keywords:
Generative AI, Large Language Models, Machine Translation, Transformers, Natural Language Processing, Long Sequence Language Models, Encoder, Decoder
[^1]
List of acronyms used in this paper. Acronym Definition AI Artificial Intelligence ASR Automatic Speech Recognition BERT Bidirectional Encoder Representations from Transformers CLIP Contrastive Language-Image Pre-training CNNs Convolutional Neural Networks DCGANs Deep Convolutional GANs DL Deep Learning DNNs Deep Neural Networks DPO Direct Policy Optimization DSM Denoising Score Matching ELBO Evidence Lower Bound FFN Position-Wise Feed Forward Network GANs Generative Adversarial Networks GELU Gaussian Error Linear Unit GPT Generative Pre-trained Transformer GPUs Graphics Processing Units HMMs Hidden Markov Models KL Kullback-Leibler LLMs Large Language Models LSTM Long Short-term Memory ML Machine Learning MLM Masked Language Modeling MoE Mixture of Experts NCE Noise-Contrastive Estimation NLG Natural Language Generation NLP Natural Language Processing NLU Natural Language Understanding ReLU Rectified Linear Unit RL Reinforcement Learning RLHF Reinforcement Learning from Human Feedback RNNs Recurrent Neural Networks TPUs Tensor Processing Units XAI Explainable Artificial Intelligence VAEs Variational Autoencoders ViT Vision Transformer WGANs Wasserstein GANs
*TABLE I: **