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Textual Intelligence
Large Language Models and Their Real-World Applications
Meenakshi Malik (Edited by), Preeti Sharma (Edited by), Susheela Hooda (Edited by)
9781394287468, Wiley
Hardback, published 5 August 2025
528 pages
28 x 19 x 2.6 cm, 0.925 kg
The book is a must-have resource for anyone looking to understand the complexities of generative AI, offering comprehensive insights into LLMs, effective training strategies, and practical applications. Textual Intelligence: Large Language Models and Their Real-World Applications provides an overview of generative AI and its multifaceted applications, as well as the significance and potential of Large Language Models (LLMs), including GPT and LLaMA. It addresses the generative AI project lifecycle, challenges in existing data architectures, proposed use case planning and scope definition, model deployment, and application integration. Training LLMs, data requirements for effective LLM training, pre-training and fine-tuning processes, and navigating computational resources and infrastructure are also discussed. The volume delves into in-context learning and prompt engineering, offering strategies for crafting effective prompts, techniques for controlling model behavior and output quality, and best practices for prompt engineering. Textual Intelligence: Large Language Models and Their Real-World Applications also discusses cost optimization strategies for LLM training, aligning models to human values, optimizing model architectures, the power of transfer learning and fine-tuning, instruction fine-tuning for precision, and parameter-efficient fine-tuning (PEFT) with adapters such as LoRA, QLoRA, and soft prompts, making it an essential guide for both beginners and industry veterans. Readers will find this book: Audience Industry professionals, academics, graduate students, and researchers seeking real-world solutions using generative AI.
Preface xix Part 1: Introduction 1 1 Introduction: Overview of Generative AI and Multifaceted Applications, Significance, and Potential of LLMs 3 1.1 Introduction to Generative AI and LLM 4 1.2 Applications of Generative AI 6 1.2.1 Medical 6 1.2.2 Education 7 1.2.3 Finance 7 1.3 Detail Case Study—Rise of Chatbots 9 1.3.1 Empowering Chatbots with Large Language Models 10 1.3.2 Chatbots in Medical and Healthcare Education 10 1.3.3 Chatbots in Finance 11 1.3.4 Chatbots in Tourism 11 1.4 Examples 12 1.5 Comparative Analysis of Generative AI Techniques 14 1.6 Future Scope and Potential 16 1.7 Conclusion 17 References 17 2 A Comprehensive Study of Large Language Models 21 2.1 Introduction 22 2.2 Background 24 2.2.1 Tokenization 24 2.2.2 Positions Encoding 24 2.2.3 Attention in LLM 25 2.2.4 Activation Function 26 2.2.5 Data Preprocessing 26 2.2.6 Architecture Model 27 2.2.7 Pre-Training 28 2.2.8 Fine-Tuning 29 2.3 Large Language Models (LLMs) 31 2.3.1 BERT (Bidirectional Encoder Representations Transformer) 31 2.3.1.1 BERT Architecture 31 2.3.1.2 Working of BERT Model 32 2.3.1.3 Fine-Tuning in BERT 33 2.3.1.4 BERT Applications 34 2.3.1.5 Advantages of the BERT Language Model 35 2.3.1.6 Disadvantages of the BERT Language Model 35 2.3.2 ChatGPT (Chat Generative Pre-Trained Transformer) 36 2.3.2.1 ChatGPT Architecture 36 2.3.2.2 Tokenization 38 2.3.2.3 Embeddings in ChatGPT 39 2.3.2.4 Pre-Training 39 2.3.2.5 Fine-Tuning 39 2.4 Challenges and Future Directions 40 2.5 Conclusion 40 References 41 Part 2: Generative AI Project Lifecycle 45 3 A Deep Learning Methodology with Transformers LLM to Calculate the Global Temperature Difference in Recent Years 47 3.1 Introduction 48 3.2 Overview of Literature IoT 50 3.3 Overview of Literature AI 53 3.4 Methodology 56 3.5 Results 57 3.6 Discussion 61 3.7 Conclusions 63 References 64 4 Navigating the Generative AI Project Ecosystem with a Focus on Addressing Data Architecture Complexities and Strategic Model Selection for Optimal Outcomes 67 4.1 Introduction 68 4.2 Literature Review 69 4.3 Proposed Method 72 4.4 Result 83 4.5 Conclusion 88 References 89 5 Generative AI Project Life Cycle—Use Case Planning and Scope Definition 93 5.1 What is Generative AI? 94 5.2 What is Artificial Intelligence? 95 5.2.1 Introduction to Generative Life Cycle 95 5.3 Generative AI on AWS 98 5.4 Why Generative AI on AWS? 99 5.5 How is Generative AI Operational? 101 5.6 Multiplicative Artificial Intelligence Interfaces 102 5.7 ChatGPT 102 5.7.1 How Does ChatGPT Work? 102 5.7.2 In What Ways is ChatGPT Being Helpful for Users? 103 5.8 What Advantages Does ChatGPT Offer? 104 5.8.1 What are ChatGPT’s limitations? To What Extent is it Accurate? 105 5.9 Dall-e 106 5.9.1 How DALL-E Works 106 5.9.2 How Do You Use DALL-E? 107 5.9.3 How is DALL-E Taught? 108 5.9.4 The Prospects of ChatGPT and Generative AI 109 5.9.5 Fields that Utilize DALL-E 110 5.9.6 Advantages of Using DALL-E to Create Images 111 5.9.7 DALL-E’s Effect on Image Production 112 5.9.8 Constraints with DALL-E 112 5.9.9 Examples of DALL-E’s Use in the Real World 113 5.9.10 What DALL-E’s Challenges Are 113 5.10 Bard 114 5.10.1 What is LaMDA? 114 5.10.2 How is Google Bard AI Used? 115 5.10.3 Google Bard AI Features 115 5.10.4 Examples and Use Cases for Google Bard AI 115 5.10.5 AI’s Reach with Google Bard 116 5.10.6 Bard AI by Google vs. ChatGPT 116 5.10.7 Constraints with Google Bard AI 117 5.10.8 Important Uses of Generative AI 118 5.10.9 Creation and Manipulation of Images 118 5.11 Coding and Software 119 5.12 Making of Videos 119 5.13 Creating and Condensing Text 119 5.14 Interorganizational Cooperation 120 5.15 Enhancement of Chatbot’s Performance 120 5.16 Business Exploration 121 5.17 Conclusion 121 References 122 6 Generative AI Unleashed: A Multi-Domain Journey of Successful Implementations of Large Language Models 125 6.1 Introduction 126 6.1.1 Background and Motivation 126 6.1.1.1 Neural Networks and Deep Learning 127 6.1.1.2 Transformers 127 6.1.1.3 Pre-Training and Fine-Tuning 127 6.1.1.4 Scaling 127 6.1.2 Scope and Objectives 128 6.2 Literature Review 128 6.2.1 Historical Development of Generative Artificial Intelligence 129 6.2.2 Evolution of LLMs 129 6.2.3 Applications of Generative AI Across Different Domains 130 6.2.4 Challenges and Limitations in Implementing LLMs 131 6.3 Methodology 131 6.3.1 Research and Design 131 6.3.2 Methods of Data Collection 131 6.3.3 Model Selection and Training Techniques 132 6.3.4 Evaluation Measures 132 6.3.5 Ethical Considerations 132 6.4 LLM-Based Case Studies 132 6.4.1 Natural Language Generation in Healthcare 133 6.4.1.1 Case Study 1: Patient Diagnosis Support System 133 6.4.1.2 Case Study 2: Electronic Health Records Summarization 134 6.4.2 Creative Content in Media and Entertainment 134 6.4.2.1 Case Study 3: A Scriptwriting Support Tool 134 6.4.2.2 Case Study 4: Developing Virtual Characters 135 6.4.3 Language Translation and Multilingual Communication 135 6.4.3.1 Case Study 5: Multilingual Communication Platform 135 6.4.3.2 Case Study 6: Real-Time Interpretation Service 136 6.5 Results and Analysis for LLMs 136 6.5.1 Performance Evaluation of Implemented Models 136 6.5.1.1 Quantitative Metrics 137 6.5.1.2 Qualitative Analysis 139 6.5.2 Impact Assessment of LLMs Across Different Domains 139 6.5.2.1 Impact Assessment of LLMs in Healthcare 140 6.5.2.2 Impact Assessment of LLMs in Infotainment 141 6.5.2.3 Impact Assessment of LLMs in Language Translation 142 6.5.3 User Feedback and Acceptance 143 6.5.3.1 A/B Testing: Choice as a Coping Strategy 144 6.5.3.2 Surveys: Capturing Broad Feedback 144 6.5.3.3 User Interviews: Getting Into the Weeds of UX 144 6.5.4 Comparison with Existing Systems 145 6.6 Discussion 145 6.6.1 Understanding the Successful Implementation of LLMs 145 6.6.1.1 Multimodal Generative AI: Unleashing the Power of Many Data Types 146 6.6.2 Challenges and Limitations 147 6.6.3 Ethical Implications and Responsible AI Practices 148 6.6.4 Future Directions and Emerging Trends 149 6.6.4.1 LLMs: A Powerful Tool, But One That Demands Careful Consideration for Society 150 6.7 Conclusion 151 References 152 Appendix 155 Glossary 155 7 Misbehaving AI Models and AI Interaction Issues with Humans 157 7.1 Introduction 158 7.2 Literature Review 160 7.3 Misbehaving AI Models 162 7.3.1 Causes of Misbehaving AI Models 162 7.3.2 Consequences of Misbehaving AI Models 164 7.3.3 Mitigation Strategies That Can Be Employed to Address Misbehaving AI Models 167 7.4 Human Interaction with AI models 168 7.4.1 Human Interaction Issues with AI Models 168 7.4.2 Laws Made to Deal with Misbehaving AI Models 169 7.4.3 The Importance of Ongoing Research and Development in Addressing Misbehaving AI Models 171 7.5 Conclusion 173 References 174 8 Decoding Potential of ChatGPT: A Comprehensive Exploration of AI Generated Contents and Challenges 177 8.1 Introduction 178 8.2 Chapter Organization 179 8.3 ChatGPT Popularity Statistics 179 8.4 Implementation and Work Flow of ChatGPT 180 8.5 ChatGPT Key Characteristics in Present Scenario 182 8.6 Potential Challenges 186 8.7 Security Threats in ChatGPT 187 8.8 ChatGPT’s Privacy Risks 189 8.9 Ethical Concern 192 8.10 Computer Ethics Challenges Raised by ChatGPT 194 8.11 Limitation of ChatGPT 195 8.12 Balance Between Human Knowledge and AI-Supported Innovation 196 8.13 Future Challenges 197 8.14 Conclusion 197 References 198 9 Economizing Large Language Model Training and Alignment with Human Values through Cost Effective Architectures and Transfer Learning Techniques 201 9.1 Introduction 202 9.2 Literature Survey 203 9.3 Proposed Method 205 9.4 Results 216 9.5 Discussion 219 9.6 Conclusion 219 References 220 Part 3: In-Context Learning/Prompt Engineering 223 10 From Prompts to Performance: Innovations in Context Learning 225 10.1 The Art of Prompt Engineering: A Deep Dive 226 10.1.1 Core Definitions and Key Concepts of Prompt Engineering 226 10.1.1.1 Significance of Prompt Engineering 226 10.1.1.2 Fundamental Components of a Prompt 226 10.1.1.3 Prompt Engineering’s Technical Aspects 228 10.2 Strategies for Crafting Effective Prompts 229 10.3 Techniques for Controlling the Model Behavior and Output 245 10.4 Best Practices for Prompt Engineering 246 10.4.1 Prompt Engineering Principles 247 10.4.2 Structured Procedure Behind Prompt Engineering 247 10.4.3 Prompt Engineering Use Cases and Applications 248 References 250 Part 4: LangChain Framework 253 11 Introduction to LangChain Framework 255 11.1 Introduction of LangChain Framework 256 11.2 Large Language Model (LLM) 258 11.3 What Do You Mean by Chains in LangChain Framework 260 11.3.1 Various Types of Chains 260 11.3.1.1 LLMChain 261 11.3.1.2 Router Chain 261 11.3.1.3 Sequential Chain 262 11.4 Why LangChain Framework is Important 263 11.5 Main Components of LangChain Framework 264 11.5.1 Large Language Model (LLM) 264 11.5.2 Prompt Template 265 11.5.2.1 Indexes 265 11.5.2.2 Retriever 265 11.5.2.3 Parsers for Output 265 11.5.2.4 Vector Store 266 11.5.2.5 Agents 266 11.5.2.6 Memory 266 11.5.2.7 Chain 267 11.6 Feature of LangChain Framework 267 11.6.1 Scalability 267 11.6.2 Improved Usability 267 11.6.3 Adaptability 267 11.6.4 Extension 267 11.6.5 External Integrations 268 11.6.6 Thriving Community 268 11.6.7 Flexibility Across Zones 268 11.6.8 Integrations 268 11.6.9 Standardized Interfaces 268 11.6.10 Prompt Management and Optimization 268 11.6.11 Visualization and Experimentation 268 11.7 How to Install 269 11.7.1 Steps to Develop an Application in LangChain Framework 270 11.7.1.1 Describe the Use Case 270 11.7.1.2 Develop Functionality 270 11.7.1.3 Tailor the Functionality 270 11.7.1.4 Optimizing LLMs 270 11.7.1.5 Data Purification 270 11.7.1.6 Experimenting 271 11.7.2 Build a New Application with LangChain Framework 271 11.8 Real World Applications with LangChain Framework 272 11.8.1 LangSmith 272 11.8.2 Chatbots 272 11.8.3 Automated Blog Outlines 272 11.8.4 Integration with MongoDB Atlas 272 11.8.5 Medical Care 272 11.8.6 Help with Coding 273 11.8.7 Creating Condensed Content 273 11.9 Integration of LangChain Framework 273 11.10 Creating a Prompt in LangChain Framework 274 11.10.1 Types of LangChain Prompts 275 11.10.2 Prompt Template 275 11.10.3 Few_Shot_Prompt_Template 276 11.10.4 Chat_Prompt_Template 276 11.11 Future of LangChain Framework with AI Enabled Tools 278 11.11.1 ChatGPT and Chatbots 278 11.11.2 AI-Powered Text Categorization Tools 278 11.11.3 False References 279 11.12 Limitation of LangChain Framework 279 11.13 Alternative Technologies Apart from LangChain Framework Used in 2024 280 11.13.1 Auto-GPT: Bringing AI Agent Development to New Heights 280 11.13.2 Prompt_Chainer 281 11.13.3 Auto_Chain 282 11.13.4 AgentGPT: Unleashing the Power of Autonomous AI Agents 282 11.13.5 BabyAGI: A Glimpse Into the Future of Task-Driven AI 283 11.13.6 SimpleaiChat 283 11.13.7 GradientJ: Building LLM-Powered Applications with Ease 284 11.14 Conclusion 284 References 285 12 LangChain: Simplifying Development with Language Models 287 12.1 Introduction 288 12.2 Phases and Characteristics of LLM Application 289 12.3 Components and Key Elements of LLM 290 12.4 Types and Architecture of LLM 293 12.5 Benefits and Approaches of LLM 296 12.6 Building an LLM Application 299 12.7 Use Cases 300 References 302 13 Addressing Ethical Challenges in LLMs: Bias and Misinformation 305 13.1 Introduction 305 13.2 LLM Evolution Tree 308 13.2.1 Bert 309 13.2.2 Gpt 311 13.3 Types of LLMs 313 13.4 Limitations of LLMs 314 13.5 Factors Contributing to Bias and Misinformation Generation 316 13.6 Methods to Address Bias and Misinformation 317 13.7 Conclusion 319 References 320 Part 5: LLM-Powered Applications 323 14 LegalEase: Application Development with LangChain Framework 325 14.1 Introduction 325 14.1.1 Large Language Model 326 14.1.2 General Architecture 327 14.1.3 Examples of LLMs 329 14.1.4 Benefits 329 14.1.5 Industry Applications 330 14.2 LangChain 331 14.2.1 Key Features of LangChain 331 14.2.2 Key Components 333 14.2.3 Who Should Explore 335 14.3 Example of Application Development 335 14.3.1 Key Features 336 14.3.2 Purpose and Benefits 336 14.4 Development Steps 337 14.4.1 Libraries and Imports 337 14.4.2 Environment Setup 340 14.4.3 Data Collection 341 14.4.4 User Interface Setup 342 14.4.5 Document Summarization 343 14.4.6 Querying the Document 355 14.5 Conclusion 362 References 363 15 Unveiling the Potential of Massive Language Models in Software Engineering: Exploring Opportunities, Addressing Risks, and Comprehending Implications 365 15.1 Introduction 366 15.2 Harnessing the Power: Abilities of Large Language Models 367 15.3 Navigating Challenges: Risks and Ethical Considerations 369 15.4 Ethical Application: Strategies and Frameworks 371 15.5 Establishing Ethical Frameworks for Accountability 372 15.6 Collaborative Standards: Industry and Research Collaboration 373 15.7 Transformative Effects: Broader Implications in Software Engineering 375 15.8 Shaping the Future: Prospective Directions of Large Language Models 377 15.9 Conclusion 378 References 379 16 Multidimensional Impacts of Generative AI and an In-Depth Analysis of LLMs with Their Expanding Horizons in Technology and Society 383 16.1 Introduction 384 16.2 Literature Review 386 16.3 Proposed Methodology 389 16.4 Results 402 16.5 Conclusion 408 References 409 Part 6: Responsible AI 413 17 Responsible AI: Ethical Considerations in Generative AI 415 17.1 Introduction 416 17.1.1 Defining Generative AI 416 17.1.2 Distinguishing Machine Learning Approaches 417 17.1.3 Brief History and Recent Breakthroughs 417 17.1.4 Overview of Key Generative Architectures and Techniques 420 17.1.4.1 Autoregressive Models 420 17.1.4.2 Generative Adversarial Networks (GANs) 420 17.1.4.3 VariationalAutoencoders (VAEs) 420 17.1.4.4 Diffusion Models 421 17.1.4.5 Self-Supervised, Meta and Multi-Task Learning 422 17.1.5 Promising Applications and Benefits 422 17.2 Key Ethical Considerations, Risks, and Challenges 423 17.2.1 Societal Biases and Unfair Representational Harms 423 17.2.2 Truth Manipulation and Attribution Difficulties 424 17.2.3 Violations of Consent, Privacy, and Agency 424 17.2.4 Misuse Potentials Across Fraud, Deceit, and Sabotage 424 17.2.5 Broader Societal Impacts on Economics, Culture and Psychology 425 17.3 Guiding Principles and Frameworks for Responsible Generative AI 425 17.3.1 Transparency 426 17.3.2 Justice, Fairness, and Inclusion 426 17.3.3 Non-Maleficence 426 17.3.4 Responsibility and Accountability 426 17.3.5 Privacy and Data Protection 426 17.4 Governance Strategies for Trustworthy Generative AI Innovation 427 17.4.1 AI Ethics Guidelines and Organizational Policies 427 17.4.2 Laws, Regulations, and Dynamic Governance Complexities 427 17.4.3 Technical Approaches to Fairness, Transparency and Control 427 17.4.4 Stakeholder Participation and Public Discourse Ethics 428 17.5 Recommendations for Key Generative AI Stakeholders 428 17.5.1 Guidelines for Technology Researchers and Developers 428 17.5.2 Strategies for Organizations, Platforms, and Corporations 429 17.5.3 Ethical Governance Strategies for Organizations 429 17.5.4 Policy Options for Governments and Lawmakers 429 17.5.5 Priorities for Broader Industry Governance Entities 430 17.5.6 Considerations for Civil Society Groups, Activists, and General Public 430 17.5.7 The Impact of Generative AI Like ChatGPT on Education 430 Significant Risks and Difficulties to Surmount 431 Research Priorities for the Future 431 17.6 Conclusions 432 References 433 18 From Prototyping to Deployment: Human-Centered Design Practices in Responsible AI Innovation 435 18.1 Introduction 436 18.2 Literature Review 441 Overview of Human-Centered Design Principles 443 Responsible AI 447 Gaps in Existing Research 451 Methodology 452 Research Design 452 Rationale for Qualitative Approach 452 Human-Centered Design in AI Prototyping 456 Distinctions and Issues 456 User Research and Personas 456 Early-Phase Prototyping 457 Iterative Design and Feedback Loops 457 Ethical Considerations in AI Prototyping 458 Identifying Ethical Challenges 458 Incorporating Ethical Guidelines Into Prototyping 458 Case Studies of Ethical AI Prototyping 459 From Prototyping to Development 459 Transitioning From Prototype to Full Development 460 Ensuring Consistency in HCD Practices 460 Collaboration Across Multidisciplinary Teams 461 Tools and Techniques for Managing Development Phases 461 Human-Centered Design in AI Deployment 462 Challenges and Solutions 463 Common Challenges in Implementing HCD in AI 463 Solutions and Best Practices 465 Lessons Learned From Case Studies 467 Framework for Human-Centered and Responsible AI 469 18.3 Conclusion 471 References 472 19 Toward Accurate Abbreviation Disambiguation in Medical Texts: A Comparative Study of AI Models 475 19.1 Introduction 476 19.2 Related Work 477 19.3 Datasets 479 19.4 Methodology 480 19.4.1 Data Collection 481 19.4.2 Pre-Processing 481 19.4.3 Vector Feature Extraction 482 19.4.4 Classification Model 484 19.5 Results and Discussion 488 19.6 Conclusion 491 References 491 Index 495
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Subject Areas: Computer science [UY]
