8 Exploring the Applications on Generative AI and LLM
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A. Ashwini
Abstract
The recent advancement in artificial intelligence (AI) - the generative artificial intelligence (GenAI) - is a most powerful form that serves to support the organizational computerized structure of society. This chapter delves into the recent methodologies and various applications relating to large language models in both scientific and technical research. This chapter mainly investigates the prime significance in enhancing the various research techniques in scientific fields. This model has significantly contributed to the creation of numerous tools by comprehending and providing the source code with natural language-based instructions. The chapter focuses on the data level incorporation that is termed to be adaptive using quantum-based techniques, which emphasize the advantages they deliver in modeling the scientific domain with comprehensive context creation. The technique required for preserving the confidentiality, transfer learning with neural network, and teamwork interaction with research work are kept under light, taking prior care on the data it provides and also the robustness that is required in the applications of AI. This chapter shows the successful applications of generative neural networks in scientific research advancements. GenAI proves to be a valuable resource for both the researchers and professionals
Abstract
The recent advancement in artificial intelligence (AI) - the generative artificial intelligence (GenAI) - is a most powerful form that serves to support the organizational computerized structure of society. This chapter delves into the recent methodologies and various applications relating to large language models in both scientific and technical research. This chapter mainly investigates the prime significance in enhancing the various research techniques in scientific fields. This model has significantly contributed to the creation of numerous tools by comprehending and providing the source code with natural language-based instructions. The chapter focuses on the data level incorporation that is termed to be adaptive using quantum-based techniques, which emphasize the advantages they deliver in modeling the scientific domain with comprehensive context creation. The technique required for preserving the confidentiality, transfer learning with neural network, and teamwork interaction with research work are kept under light, taking prior care on the data it provides and also the robustness that is required in the applications of AI. This chapter shows the successful applications of generative neural networks in scientific research advancements. GenAI proves to be a valuable resource for both the researchers and professionals
Kapitel in diesem Buch
- Frontmatter I
- Preface V
- Contents VII
- About the Editors IX
- List of Contributors XI
- 1 Unveiling the Power of Generative AI: A Journey into Large Language Models 1
- 2 Early Roots of Generative AI Models and LLM: A Diverse Landscape 23
- 3 Generative AI Models and LLM: Training Techniques and Evaluation Metrics 43
- 4 Importance of Prompt Engineering in Generative AI Models 69
- 5 LLM Pretraining Methods 93
- 6 LLM Fine-Tuning: Instruction and Parameter-Efficient Fine-Tuning (PEFT) 117
- 7 Reinforcement Learning from Human Feedback (RLHF) 135
- 8 Exploring the Applications on Generative AI and LLM 155
- 9 Bias and Fairness in Generative AI 177
- 10 Future Directions and Open Problems in Generative AI 193
- 11 Optimizing Sustainable Project Management Life Cycle Using Generative AI Modeling 213
- 12 Generative AI and LLM: Case Study in Finance 231
- 13 Generative AI and LLM: Case Study in E-Commerce 253
- Index 273
Kapitel in diesem Buch
- Frontmatter I
- Preface V
- Contents VII
- About the Editors IX
- List of Contributors XI
- 1 Unveiling the Power of Generative AI: A Journey into Large Language Models 1
- 2 Early Roots of Generative AI Models and LLM: A Diverse Landscape 23
- 3 Generative AI Models and LLM: Training Techniques and Evaluation Metrics 43
- 4 Importance of Prompt Engineering in Generative AI Models 69
- 5 LLM Pretraining Methods 93
- 6 LLM Fine-Tuning: Instruction and Parameter-Efficient Fine-Tuning (PEFT) 117
- 7 Reinforcement Learning from Human Feedback (RLHF) 135
- 8 Exploring the Applications on Generative AI and LLM 155
- 9 Bias and Fairness in Generative AI 177
- 10 Future Directions and Open Problems in Generative AI 193
- 11 Optimizing Sustainable Project Management Life Cycle Using Generative AI Modeling 213
- 12 Generative AI and LLM: Case Study in Finance 231
- 13 Generative AI and LLM: Case Study in E-Commerce 253
- Index 273
