Generative AI and LLMs Natural Language Processing and Generative Adversarial Networks

Title Page Copyright Contents Preface About the Editors List of Contributors 1 Unveiling the Power of Generative AI: A Journey into Large Language Models 1.1 Overview of Generative AI and Large Language Models 1.2 Fundamental Concepts 1.3 Algorithms Used in Generative Models 1.4 Text Generation 1.5 Pretraining and Fine-Tuning of LLM Models 1.6 Impact on Generative AI and LLM 1.7 Application of LLMs 1.8 Challenges and Limitations 1.9 Future Directions 1.10 Conclusion References 2 Early Roots of Generative AI Models and LLM: A Diverse Landscape 2.1 Introduction to Rule-Based Approaches 2.2 Emergence of Statistical Language Models 2.3 Early Experiments on Neural Network 2.4 Pioneering Architectures in Language Modeling 2.5 Integration of Expert Systems with Language Models 2.6 Impact on Early Generative AI 2.7 Theoretical Foundations and Hybrid Approaches 2.8 Limitations and Challenges 2.9 Bridge to Modern Large Language Models (LLMs) 2.10 Conclusion References 3 Generative AI Models and LLM: Training Techniques and Evaluation Metrics 3.1 Introduction 3.2 Generative AI Model and LLM Training Techniques 3.3 Variational Autoencoder 3.4 Transformer Models 3.5 LangChain 3.6 Diffusion Model 3.7 Flow-Based Models 3.8 Evaluation Metrics 3.9 Conclusion References 4 Importance of Prompt Engineering in Generative AI Models 4.1 Introduction 4.2 Theoretical Underpinnings of Prompt Engineering 4.3 Methodologies in Prompt Engineering 4.4 Empirical Studies and Case Examples 4.5 Examining the Influence of Prompts: Multidisciplinary Views 4.6 Interdisciplinary Perspectives on Prompt Engineering 4.7 Future Directions and Challenges 4.7 Emerging Trends in Prompt Engineering 4.8 Prospects and Difficulties 4.9 Conclusion References 5 LLM Pretraining Methods 5.1 Introduction 5.2 Steps for Training LLM Models 5.3 Study of Pretraining in LLM 5.4 Effect of Pretraining on LLM 5.5 Key Considerations for Pretraining LLM 5.6 Characteristics of LLM Pretraining 5.7 Some Use Cases of LLM Pretraining 5.8 Summary References 6 LLM Fine-Tuning: Instruction and Parameter-Efficient Fine-Tuning (PEFT) 6.1 Introduction 6.2 LLM Fine-Tuning: Instruction and Parameter Efficient Fine-Tuning 6.3 Reinforcement Learning from Human Feedback (RLHF) 6.4 Parameter-Efficient Fine-Tuning (PEFT) 6.5 PEFT Methods 6.6 LoRA: Low-Rank Adaption Method 6.7 QLoRA: Quantized Low-Rank Adaption Method 6.8 Conclusion References 7 Reinforcement Learning from Human Feedback (RLHF) 7.1 Introduction 7.2 Foundations of Reinforcement Learning 7.3 Transitioning to RLHF 7.4 Impact of RLHF on Tailoring LLMs: Case Studies 7.5 Ethical Considerations in RLHF for LLMs 7.6 RLHF Derivatives 7.7 Conclusion References 8 Exploring the Applications on Generative AI and LLM 8.1 Overview to Generative AI 8.2 Meta Learning Fundamentals for Adaptive Scientific Modeling 8.3 Automatic Hypothesis Generation with Generative Models 8.4 Quantum Computing Concepts in Generative Models 8.5 Real-Time Collaboration with Generative Models 8.6 Implementation of Privacy-Preserving Techniques 8.7 Enhancing Scientific Visualization Techniques 8.8 Leveraging Blockchain for Trust and Transparency 8.9 Conclusion and Future Directions References 9 Bias and Fairness in Generative AI 9.1 Introduction 9.2 Bias: Sources, Impact, and Mitigation Strategies 9.3 Fairness: Metrics and Mitigation Strategies 9.4 Conclusion References 10 Future Directions and Open Problems in Generative AI 10.1 Introduction 10.2 Importance of Exploring GenAI 10.3 Improving Control and Interpretability in Generative AI 10.4 Ethical Challenges in Generative AI 10.5 Expanding Generative Frameworks 10.6 Semantic Gap 10.7 Innovative Architectures 10.8 Research Areas in Generative AI 10.9 Industry Perspectives and Case Studies 10.10 Conclusion References 11 Optimizing Sustainable Project Management Life Cycle Using Generative AI Modeling 11.1 Introduction 11.2 Literature Review 11.3 Current Issues in Project/Product Life Cycle Management Using GenAI 11.4 Optimizing the GenAI Made for Edge Devices in the Near Future 11.5 Conclusion References 12 Generative AI and LLM: Case Study in Finance 12.1 Introduction 12.2 Challenges and Ethical Considerations for Language Models in Finance 12.3 Major FinTech Models 12.4 Conclusion and Future Directions References 13 Generative AI and LLM: Case Study in E-Commerce 13.1 Introduction 13.2 Significance of AI in E-Commerce 13.3 Case Studies 13.4 Implementation Strategies 13.5 Future Trends in E-Commerce 13.6 Conclusion References Index