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| few-shot learning with retrieval augmented language models: Proceedings of the 2023 3rd International Conference on Education, Information Management and Service Science (EIMSS 2023) Guiyun Guan, Christian Kahl, Bootheina Majoul, Deepanjali Mishra, 2023-10-29 This is an open access book.Amidst the advancement of modern science and technology, especially the development of information technology, our society has entered a stage of highly developed information technology. We should do our utmost to utilize the achievements yielded by scientific and technological innovation, vigorously promote the informatization of education management, and provide quality services for education and teaching. The importance of information technology education in educational management simply cannot be overstated. Educational management is closely related to college education and teaching. Only through good educational management can education and teaching proceed smoothly. The realization of education management information is conducive to the propulsion of high efficiency in school management, as well as to the smooth implementation of teaching objectives and better participation of students and parents in school management. Informationization is the mainstream of the world's economic development, while informationization of teaching management is the product of adapting to the demand of time development. We educational management workers should learn from the excellent educational managers at home and abroad, strive to improve their information level, and synchronize with the Times. In order to provide a more convenient and efficient communication platform for relevant academic researchers, we organized the 2023 3rd International Conference on Education, Information Management and Service Science (EIMSS 2023). 2023 3rd International Conference on Education, Information Management and Service Science (EIMSS 2023) will be held on July 21–23, 2023 in Qingdao, China. EIMSS 2023 aims to bring together innovative academics and industrial experts in the field of Education, Information Management and Service Science to a common forum. The primary goal of the conference is to stimulate research and developmental activities in Education, Information Management and Service Science, and another goal is to facilitate the scientific exchange of information between researchers, developers, engineers, students, and practitioners working all around the world. As an ideal platform for individuals to exchange views and experiences in Education, Information Management, Service Science, and related domains, the conference will convene annually. We warmly invite you to participate in EIMSS 2023 and look forward to seeing you in Qingdao! |
| few-shot learning with retrieval augmented language models: Medical Image Computing and Computer Assisted Intervention – MICCAI 2024 Marius George Linguraru, |
| few-shot learning with retrieval augmented language models: Pretrain Vision and Large Language Models in Python Emily Webber, Andrea Olgiati, 2023-05-31 Master the art of training vision and large language models with conceptual fundaments and industry-expert guidance. Learn about AWS services and design patterns, with relevant coding examples Key Features Learn to develop, train, tune, and apply foundation models with optimized end-to-end pipelines Explore large-scale distributed training for models and datasets with AWS and SageMaker examples Evaluate, deploy, and operationalize your custom models with bias detection and pipeline monitoring Book Description Foundation models have forever changed machine learning. From BERT to ChatGPT, CLIP to Stable Diffusion, when billions of parameters are combined with large datasets and hundreds to thousands of GPUs, the result is nothing short of record-breaking. The recommendations, advice, and code samples in this book will help you pretrain and fine-tune your own foundation models from scratch on AWS and Amazon SageMaker, while applying them to hundreds of use cases across your organization. With advice from seasoned AWS and machine learning expert Emily Webber, this book helps you learn everything you need to go from project ideation to dataset preparation, training, evaluation, and deployment for large language, vision, and multimodal models. With step-by-step explanations of essential concepts and practical examples, you'll go from mastering the concept of pretraining to preparing your dataset and model, configuring your environment, training, fine-tuning, evaluating, deploying, and optimizing your foundation models. You will learn how to apply the scaling laws to distributing your model and dataset over multiple GPUs, remove bias, achieve high throughput, and build deployment pipelines. By the end of this book, you'll be well equipped to embark on your own project to pretrain and fine-tune the foundation models of the future. What you will learn Find the right use cases and datasets for pretraining and fine-tuning Prepare for large-scale training with custom accelerators and GPUs Configure environments on AWS and SageMaker to maximize performance Select hyperparameters based on your model and constraints Distribute your model and dataset using many types of parallelism Avoid pitfalls with job restarts, intermittent health checks, and more Evaluate your model with quantitative and qualitative insights Deploy your models with runtime improvements and monitoring pipelines Who this book is for If you're a machine learning researcher or enthusiast who wants to start a foundation modelling project, this book is for you. Applied scientists, data scientists, machine learning engineers, solution architects, product managers, and students will all benefit from this book. Intermediate Python is a must, along with introductory concepts of cloud computing. A strong understanding of deep learning fundamentals is needed, while advanced topics will be explained. The content covers advanced machine learning and cloud techniques, explaining them in an actionable, easy-to-understand way. |
| few-shot learning with retrieval augmented language models: Text, Speech, and Dialogue Elmar Nöth, |
| few-shot learning with retrieval augmented language models: Generative AI for Effective Software Development Anh Nguyen-Duc, |
| few-shot learning with retrieval augmented language models: Natural Language Processing and Chinese Computing Derek F. Wong, |
| few-shot learning with retrieval augmented language models: Computer Vision – ECCV 2024 Aleš Leonardis, |
| few-shot learning with retrieval augmented language models: Generative AI on AWS Chris Fregly, Antje Barth, Shelbee Eigenbrode, 2023-11-13 Companies today are moving rapidly to integrate generative AI into their products and services. But there's a great deal of hype (and misunderstanding) about the impact and promise of this technology. With this book, Chris Fregly, Antje Barth, and Shelbee Eigenbrode from AWS help CTOs, ML practitioners, application developers, business analysts, data engineers, and data scientists find practical ways to use this exciting new technology. You'll learn the generative AI project life cycle including use case definition, model selection, model fine-tuning, retrieval-augmented generation, reinforcement learning from human feedback, and model quantization, optimization, and deployment. And you'll explore different types of models including large language models (LLMs) and multimodal models such as Stable Diffusion for generating images and Flamingo/IDEFICS for answering questions about images. Apply generative AI to your business use cases Determine which generative AI models are best suited to your task Perform prompt engineering and in-context learning Fine-tune generative AI models on your datasets with low-rank adaptation (LoRA) Align generative AI models to human values with reinforcement learning from human feedback (RLHF) Augment your model with retrieval-augmented generation (RAG) Explore libraries such as LangChain and ReAct to develop agents and actions Build generative AI applications with Amazon Bedrock |
| few-shot learning with retrieval augmented language models: Developing Apps with GPT-4 and ChatGPT Olivier Caelen, Marie-Alice Blete, 2024-07-10 This book provides an ideal guide for Python developers who want to learn how to build applications with large language models. Authors Olivier Caelen and Marie-Alice Blete cover the main features and benefits of GPT-4 and GPT-3.5 models and explain how they work. You'll also get a step-by-step guide for developing applications using the OpenAI Python library, including text generation, Q&A, and smart assistants. Written in clear and concise language, Developing Apps with GPT-4 and ChatGPT includes easy-to-follow examples to help you understand and apply the concepts to your projects. Python code examples are available in a GitHub repository, and the book includes a glossary of key terms. Ready to harness the power of large language models in your applications? This book is a must. You'll learn: Fundamentals and benefits of GPT-4 and GPT-3.5 models, including the main features and how they work How to integrate these models into Python-based applications, leveraging natural language processing capabilities and overcoming specific LLM-related challenges Examples of applications demonstrating the OpenAI API in Python for tasks including text generation, question answering, content summarization, classification, and more Advanced LLM topics such as prompt engineering, fine-tuning models for specific tasks, RAG, plug-ins, LangChain, LlamaIndex, GPTs, and assistants Olivier Caelen is a machine learning researcher at Worldline and teaches machine learning courses at the University of Brussels. Marie-Alice Blete, a software architect and data engineer in Worldline's R&D department, is interested in performance and latency issues associated with AI solutions. |
| few-shot learning with retrieval augmented language models: Quick Start Guide to Large Language Models Sinan Ozdemir, 2024-09-26 The Practical, Step-by-Step Guide to Using LLMs at Scale in Projects and Products Large Language Models (LLMs) like Llama 3, Claude 3, and the GPT family are demonstrating breathtaking capabilities, but their size and complexity have deterred many practitioners from applying them. In Quick Start Guide to Large Language Models, Second Edition, pioneering data scientist and AI entrepreneur Sinan Ozdemir clears away those obstacles and provides a guide to working with, integrating, and deploying LLMs to solve practical problems. Ozdemir brings together all you need to get started, even if you have no direct experience with LLMs: step-by-step instructions, best practices, real-world case studies, and hands-on exercises. Along the way, he shares insights into LLMs' inner workings to help you optimize model choice, data formats, prompting, fine-tuning, performance, and much more. The resources on the companion website include sample datasets and up-to-date code for working with open- and closed-source LLMs such as those from OpenAI (GPT-4 and GPT-3.5), Google (BERT, T5, and Gemini), X (Grok), Anthropic (the Claude family), Cohere (the Command family), and Meta (BART and the LLaMA family). Learn key concepts: pre-training, transfer learning, fine-tuning, attention, embeddings, tokenization, and more Use APIs and Python to fine-tune and customize LLMs for your requirements Build a complete neural/semantic information retrieval system and attach to conversational LLMs for building retrieval-augmented generation (RAG) chatbots and AI Agents Master advanced prompt engineering techniques like output structuring, chain-of-thought prompting, and semantic few-shot prompting Customize LLM embeddings to build a complete recommendation engine from scratch with user data that outperforms out-of-the-box embeddings from OpenAI Construct and fine-tune multimodal Transformer architectures from scratch using open-source LLMs and large visual datasets Align LLMs using Reinforcement Learning from Human and AI Feedback (RLHF/RLAIF) to build conversational agents from open models like Llama 3 and FLAN-T5 Deploy prompts and custom fine-tuned LLMs to the cloud with scalability and evaluation pipelines in mind Diagnose and optimize LLMs for speed, memory, and performance with quantization, probing, benchmarking, and evaluation frameworks A refreshing and inspiring resource. Jam-packed with practical guidance and clear explanations that leave you smarter about this incredible new field. --Pete Huang, author of The Neuron Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details. |
| few-shot learning with retrieval augmented language models: Advances in Multimodal Information Retrieval and Generation Man Luo, |
| few-shot learning with retrieval augmented language models: Artificial Intelligence in Education Andrew M. Olney, |
| few-shot learning with retrieval augmented language models: Representation Learning for Natural Language Processing Zhiyuan Liu, Yankai Lin, Maosong Sun, 2023-08-23 This book provides an overview of the recent advances in representation learning theory, algorithms, and applications for natural language processing (NLP), ranging from word embeddings to pre-trained language models. It is divided into four parts. Part I presents the representation learning techniques for multiple language entries, including words, sentences and documents, as well as pre-training techniques. Part II then introduces the related representation techniques to NLP, including graphs, cross-modal entries, and robustness. Part III then introduces the representation techniques for the knowledge that are closely related to NLP, including entity-based world knowledge, sememe-based linguistic knowledge, legal domain knowledge and biomedical domain knowledge. Lastly, Part IV discusses the remaining challenges and future research directions. The theories and algorithms of representation learning presented can also benefit other related domains such as machine learning, social network analysis, semantic Web, information retrieval, data mining and computational biology. This book is intended for advanced undergraduate and graduate students, post-doctoral fellows, researchers, lecturers, and industrial engineers, as well as anyone interested in representation learning and natural language processing. As compared to the first edition, the second edition (1) provides a more detailed introduction to representation learning in Chapter 1; (2) adds four new chapters to introduce pre-trained language models, robust representation learning, legal knowledge representation learning and biomedical knowledge representation learning; (3) updates recent advances in representation learning in all chapters; and (4) corrects some errors in the first edition. The new contents will be approximately 50%+ compared to the first edition. This is an open access book. |
| few-shot learning with retrieval augmented language models: The Deep Learning Architect's Handbook Ee Kin Chin, 2023-12-29 Harness the power of deep learning to drive productivity and efficiency using this practical guide covering techniques and best practices for the entire deep learning life cycle Key Features Interpret your models’ decision-making process, ensuring transparency and trust in your AI-powered solutions Gain hands-on experience in every step of the deep learning life cycle Explore case studies and solutions for deploying DL models while addressing scalability, data drift, and ethical considerations Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionDeep learning enables previously unattainable feats in automation, but extracting real-world business value from it is a daunting task. This book will teach you how to build complex deep learning models and gain intuition for structuring your data to accomplish your deep learning objectives. This deep learning book explores every aspect of the deep learning life cycle, from planning and data preparation to model deployment and governance, using real-world scenarios that will take you through creating, deploying, and managing advanced solutions. You’ll also learn how to work with image, audio, text, and video data using deep learning architectures, as well as optimize and evaluate your deep learning models objectively to address issues such as bias, fairness, adversarial attacks, and model transparency. As you progress, you’ll harness the power of AI platforms to streamline the deep learning life cycle and leverage Python libraries and frameworks such as PyTorch, ONNX, Catalyst, MLFlow, Captum, Nvidia Triton, Prometheus, and Grafana to execute efficient deep learning architectures, optimize model performance, and streamline the deployment processes. You’ll also discover the transformative potential of large language models (LLMs) for a wide array of applications. By the end of this book, you'll have mastered deep learning techniques to unlock its full potential for your endeavors.What you will learn Use neural architecture search (NAS) to automate the design of artificial neural networks (ANNs) Implement recurrent neural networks (RNNs), convolutional neural networks (CNNs), BERT, transformers, and more to build your model Deal with multi-modal data drift in a production environment Evaluate the quality and bias of your models Explore techniques to protect your model from adversarial attacks Get to grips with deploying a model with DataRobot AutoML Who this book is for This book is for deep learning practitioners, data scientists, and machine learning developers who want to explore deep learning architectures to solve complex business problems. Professionals in the broader deep learning and AI space will also benefit from the insights provided, applicable across a variety of business use cases. Working knowledge of Python programming and a basic understanding of deep learning techniques is needed to get started with this book. |
| few-shot learning with retrieval augmented language models: Building Intelligent Applications with Generative AI Yattish Ramhorry, 2024-08-22 DESCRIPTION Building Intelligent Applications with Generative AI is a comprehensive guide that unlocks the power of generative AI for building cutting-edge applications. This book covers a wide range of use cases and practical examples, from text generation and conversational agents to creative media generation and code completion. These examples are designed to help you capitalize on the potential of generative AI in your applications. Through clear explanations, step-by-step tutorials, and real-world case studies, you will learn how to prepare data and train generative AI models. You will also explore different generative AI techniques, including large language models like GPT-4, ChatGPT, Llama 2, and Google’s Gemini, to understand how they can be applied in various domains, such as content generation, virtual assistants, and code generation. With a focus on practical implementation, this book also examines ethical considerations, best practices, and future trends in generative AI. Further, this book concludes by exploring ethical considerations and best practices for building responsible GAI applications, ensuring you are harnessing this technology for good. By the end of this book, you will be well-equipped to leverage the power of GAI to build intelligent applications and unleash your creativity in innovative ways. KEY FEATURES ● Learn the fundamentals of generative AI and the practical usage of prompt engineering. ● Gain hands-on experience in building generative AI applications. ● Learn to use tools like LangChain, LangSmith, and FlowiseAI to create intelligent applications and AI chatbots. WHAT YOU WILL LEARN ● Understand generative AI (GAI) and large language models (LLMs). ● Explore real-world GAI applications across industries. ● Build intelligent applications with the ChatGPT API. ● Explore retrieval augmented generation with LangChain and Gemini Pro. ● Create chatbots with LangChain and Streamlit for data retrieval. WHO THIS BOOK IS FOR This book is for developers, data scientists, AI practitioners, and tech enthusiasts who are interested in leveraging generative AI techniques to build intelligent applications across various domains. TABLE OF CONTENTS 1. Exploring the World of Generative AI 2. Use Cases for Generative AI Applications 3. Mastering the Art of Prompt Engineering 4. Integrating Generative AI Models into Applications 5. Emerging Trends and the Future of Generative AI 6. Building Intelligent Applications with the ChatGPT API 7. Retrieval Augmented Generation with Gemini Pro 8. Generative AI Applications with Gradio 9. Visualize your Data with LangChain and Streamlit 10. Building LLM Applications with Llama 2 11. Building an AI Document Chatbot with Flowise AI 12. Best Practices for Building Applications with Generative AI 13. Ethical Considerations of Generative AI |
| few-shot learning with retrieval augmented language models: Hands-On Large Language Models Jay Alammar, Maarten Grootendorst, 2024-09-11 AI has acquired startling new language capabilities in just the past few years. Driven by the rapid advances in deep learning, language AI systems are able to write and understand text better than ever before. This trend enables the rise of new features, products, and entire industries. With this book, Python developers will learn the practical tools and concepts they need to use these capabilities today. You'll learn how to use the power of pre-trained large language models for use cases like copywriting and summarization; create semantic search systems that go beyond keyword matching; build systems that classify and cluster text to enable scalable understanding of large amounts of text documents; and use existing libraries and pre-trained models for text classification, search, and clusterings. This book also shows you how to: Build advanced LLM pipelines to cluster text documents and explore the topics they belong to Build semantic search engines that go beyond keyword search with methods like dense retrieval and rerankers Learn various use cases where these models can provide value Understand the architecture of underlying Transformer models like BERT and GPT Get a deeper understanding of how LLMs are trained Understanding how different methods of fine-tuning optimize LLMs for specific applications (generative model fine-tuning, contrastive fine-tuning, in-context learning, etc.) |
| few-shot learning with retrieval augmented language models: Advances in Information Retrieval Nazli Goharian, |
| few-shot learning with retrieval augmented language models: Large Language Models Uday Kamath, Kevin Keenan, Garrett Somers, Sarah Sorenson, 2024 Large Language Models (LLMs) have emerged as a cornerstone technology, transforming how we interact with information and redefining the boundaries of artificial intelligence. LLMs offer an unprecedented ability to understand, generate, and interact with human language in an intuitive and insightful manner, leading to transformative applications across domains like content creation, chatbots, search engines, and research tools. While fascinating, the complex workings of LLMs -- their intricate architecture, underlying algorithms, and ethical considerations -- require thorough exploration, creating a need for a comprehensive book on this subject. This book provides an authoritative exploration of the design, training, evolution, and application of LLMs. It begins with an overview of pre-trained language models and Transformer architectures, laying the groundwork for understanding prompt-based learning techniques. Next, it dives into methods for fine-tuning LLMs, integrating reinforcement learning for value alignment, and the convergence of LLMs with computer vision, robotics, and speech processing. The book strongly emphasizes practical applications, detailing real-world use cases such as conversational chatbots, retrieval-augmented generation (RAG), and code generation. These examples are carefully chosen to illustrate the diverse and impactful ways LLMs are being applied in various industries and scenarios. Readers will gain insights into operationalizing and deploying LLMs, from implementing modern tools and libraries to addressing challenges like bias and ethical implications. The book also introduces the cutting-edge realm of multimodal LLMs that can process audio, images, video, and robotic inputs. With hands-on tutorials for applying LLMs to natural language tasks, this thorough guide equips readers with both theoretical knowledge and practical skills for leveraging the full potential of large language models. This comprehensive resource is appropriate for a wide audience: students, researchers and academics in AI or NLP, practicing data scientists, and anyone looking to grasp the essence and intricacies of LLMs. |
| few-shot learning with retrieval augmented language models: Document Analysis and Recognition - ICDAR 2024 Elisa H. Barney Smith, |
| few-shot learning with retrieval augmented language models: Large Language Models Oswald Campesato, 2024-10-02 This book begins with an overview of the Generative AI landscape, distinguishing it from conversational AI and shedding light on the roles of key players like DeepMind and OpenAI. It then reviews the intricacies of ChatGPT, GPT-4, and Gemini, examining their capabilities, strengths, and competitors. Readers will also gain insights into the BERT family of LLMs, including ALBERT, DistilBERT, and XLNet, and how these models have revolutionized natural language processing. Further, the book covers prompt engineering techniques, essential for optimizing the outputs of AI models, and addresses the challenges of working with LLMs, including the phenomenon of hallucinations and the nuances of fine-tuning these advanced models. Designed for software developers, AI researchers, and technology enthusiasts with a foundational understanding of AI, this book offers both theoretical insights and practical code examples in Python. Companion files with code, figures, and datasets are available for downloading from the publisher. |
| few-shot learning with retrieval augmented language models: Hardware Security Mark Tehranipoor, |
| few-shot learning with retrieval augmented language models: Generative AI Foundations in Python Carlos Rodriguez, 2024-07-26 Begin your generative AI journey with Python as you explore large language models, understand responsible generative AI practices, and apply your knowledge to real-world applications through guided tutorials Key Features Gain expertise in prompt engineering, LLM fine-tuning, and domain adaptation Use transformers-based LLMs and diffusion models to implement AI applications Discover strategies to optimize model performance, address ethical considerations, and build trust in AI systems Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionThe intricacies and breadth of generative AI (GenAI) and large language models can sometimes eclipse their practical application. It is pivotal to understand the foundational concepts needed to implement generative AI. This guide explains the core concepts behind -of-the-art generative models by combining theory and hands-on application. Generative AI Foundations in Python begins by laying a foundational understanding, presenting the fundamentals of generative LLMs and their historical evolution, while also setting the stage for deeper exploration. You’ll also understand how to apply generative LLMs in real-world applications. The book cuts through the complexity and offers actionable guidance on deploying and fine-tuning pre-trained language models with Python. Later, you’ll delve into topics such as task-specific fine-tuning, domain adaptation, prompt engineering, quantitative evaluation, and responsible AI, focusing on how to effectively and responsibly use generative LLMs. By the end of this book, you’ll be well-versed in applying generative AI capabilities to real-world problems, confidently navigating its enormous potential ethically and responsibly.What you will learn Discover the fundamentals of GenAI and its foundations in NLP Dissect foundational generative architectures including GANs, transformers, and diffusion models Find out how to fine-tune LLMs for specific NLP tasks Understand transfer learning and fine-tuning to facilitate domain adaptation, including fields such as finance Explore prompt engineering, including in-context learning, templatization, and rationalization through chain-of-thought and RAG Implement responsible practices with generative LLMs to minimize bias, toxicity, and other harmful outputs Who this book is for This book is for developers, data scientists, and machine learning engineers embarking on projects driven by generative AI. A general understanding of machine learning and deep learning, as well as some proficiency with Python, is expected. |
| few-shot learning with retrieval augmented language models: Artificial Intelligence in Medicine Joseph Finkelstein, |
| few-shot learning with retrieval augmented language models: Foundation Models for Natural Language Processing Gerhard Paaß, Sven Giesselbach, 2023-05-23 This open access book provides a comprehensive overview of the state of the art in research and applications of Foundation Models and is intended for readers familiar with basic Natural Language Processing (NLP) concepts. Over the recent years, a revolutionary new paradigm has been developed for training models for NLP. These models are first pre-trained on large collections of text documents to acquire general syntactic knowledge and semantic information. Then, they are fine-tuned for specific tasks, which they can often solve with superhuman accuracy. When the models are large enough, they can be instructed by prompts to solve new tasks without any fine-tuning. Moreover, they can be applied to a wide range of different media and problem domains, ranging from image and video processing to robot control learning. Because they provide a blueprint for solving many tasks in artificial intelligence, they have been called Foundation Models. After a brief introduction to basic NLP models the main pre-trained language models BERT, GPT and sequence-to-sequence transformer are described, as well as the concepts of self-attention and context-sensitive embedding. Then, different approaches to improving these models are discussed, such as expanding the pre-training criteria, increasing the length of input texts, or including extra knowledge. An overview of the best-performing models for about twenty application areas is then presented, e.g., question answering, translation, story generation, dialog systems, generating images from text, etc. For each application area, the strengths and weaknesses of current models are discussed, and an outlook on further developments is given. In addition, links are provided to freely available program code. A concluding chapter summarizes the economic opportunities, mitigation of risks, and potential developments of AI. |
| few-shot learning with retrieval augmented language models: Networked Systems Armando Castañeda, |
| few-shot learning with retrieval augmented language models: Web and Big Data Xiangyu Song, |
| few-shot learning with retrieval augmented language models: Advanced Information Systems Engineering Workshops João Paulo A. Almeida, |
| few-shot learning with retrieval augmented language models: Open-Domain Question Answering John Prager, 2007 Open-Domain Question Answering is an introduction to the field of Question Answering (QA). It covers the basic principles of QA along with a selection of systems that have exhibited interesting and significant techniques, so it serves more as a tutorial than as an exhaustive survey of the field. Starting with a brief history of the field, it goes on to describe the architecture of a QA system before analysing in detail some of the specific approaches that have been successfully deployed by academia and industry designing and building such systems. Open-Domain Question Answering is both a guide for beginners who are embarking on research in this area, and a useful reference for established researchers and practitioners in this field. |
| few-shot learning with retrieval augmented language models: Generative AI and Implications for Ethics, Security, and Data Management Gomathi Sankar, Jeganathan, David, Arokiaraj, 2024-08-21 As generative AI rapidly advances with the field of artificial intelligence, its presence poses significant ethical, security, and data management challenges. While this technology encourages innovation across various industries, ethical concerns regarding the potential misuse of AI-generated content for misinformation or manipulation may arise. The risks of AI-generated deepfakes and cyberattacks demand more research into effective security tactics. The supervision of datasets required to train generative AI models raises questions about privacy, consent, and responsible data management. As generative AI evolves, further research into the complex issues regarding its potential is required to safeguard ethical values and security of people’s data. Generative AI and Implications for Ethics, Security, and Data Management explores the implications of generative AI across various industries who may use the tool for improved organizational development. The security and data management benefits of generative AI are outlined, while examining the topic within the lens of ethical and social impacts. This book covers topics such as cybersecurity, digital technology, and cloud storage, and is a useful resource for computer engineers, IT professionals, technicians, sociologists, healthcare workers, researchers, scientists, and academicians. |
| few-shot learning with retrieval augmented language models: Dependency Parsing Sandra Kübler, Ryan McDonald, Joakim Nivre, 2009 Dependency-based methods for syntactic parsing have become increasingly popular in natural language processing in recent years. This book gives a thorough introduction to the methods that are most widely used today. After an introduction to dependency grammar and dependency parsing, followed by a formal characterization of the dependency parsing problem, the book surveys the three major classes of parsing models that are in current use: transition-based, graph-based, and grammar-based models. It continues with a chapter on evaluation and one on the comparison of different methods, and it closes with a few words on current trends and future prospects of dependency parsing. The book presupposes a knowledge of basic concepts in linguistics and computer science, as well as some knowledge of parsing methods for constituency-based representations. Table of Contents: Introduction / Dependency Parsing / Transition-Based Parsing / Graph-Based Parsing / Grammar-Based Parsing / Evaluation / Comparison / Final Thoughts |
| few-shot learning with retrieval augmented language models: Generative AI Security Ken Huang, |
| few-shot learning with retrieval augmented language models: LINKING THEORY AND PRACTICE OF DIGITAL LIBRARIES Apostolos Antonacopoulos, 2024 |
| few-shot learning with retrieval augmented language models: Big Data and Artificial Intelligence Vikram Goyal, Naveen Kumar, Sourav S. Bhowmick, Pawan Goyal, Navneet Goyal, Dhruv Kumar, 2023-12-04 This book constitutes the proceedings of the 11th International Conference on Big Data and Artificial Intelligence, BDA 2023, held in Delhi, India, during December 7–9, 2023. The17 full papers presented in this volume were carefully reviewed and selected from 67 submissions. The papers are organized in the following topical sections: Keynote Lectures, Artificial Intelligence in Healthcare, Large Language Models, Data Analytics for Low Resource Domains, Artificial Intelligence for Innovative Applications and Potpourri. |
| few-shot learning with retrieval augmented language models: Prompt Engineering for Generative AI James Phoenix, Mike Taylor, 2024-05-16 Large language models (LLMs) and diffusion models such as ChatGPT and Stable Diffusion have unprecedented potential. Because they have been trained on all the public text and images on the internet, they can make useful contributions to a wide variety of tasks. And with the barrier to entry greatly reduced today, practically any developer can harness LLMs and diffusion models to tackle problems previously unsuitable for automation. With this book, you'll gain a solid foundation in generative AI, including how to apply these models in practice. When first integrating LLMs and diffusion models into their workflows, most developers struggle to coax reliable enough results from them to use in automated systems. Authors James Phoenix and Mike Taylor show you how a set of principles called prompt engineering can enable you to work effectively with AI. Learn how to empower AI to work for you. This book explains: The structure of the interaction chain of your program's AI model and the fine-grained steps in between How AI model requests arise from transforming the application problem into a document completion problem in the model training domain The influence of LLM and diffusion model architecture—and how to best interact with it How these principles apply in practice in the domains of natural language processing, text and image generation, and code |
| few-shot learning with retrieval augmented language models: Advances in Information Retrieval Jaap Kamps, Lorraine Goeuriot, Fabio Crestani, Maria Maistro, Hideo Joho, Brian Davis, Cathal Gurrin, Udo Kruschwitz, Annalina Caputo, 2023-03-16 The three-volume set LNCS 13980, 13981 and 13982 constitutes the refereed proceedings of the 45th European Conference on IR Research, ECIR 2023, held in Dublin, Ireland, during April 2-6, 2023. The 65 full papers, 41 short papers, 19 demonstration papers, 12 reproducibility papers consortium papers, 7 tutorial papers, and 10 doctorial consortium papers were carefully reviewed and selected from 489 submissions. The book also contains, 8 workshop summaries and 13 CLEF Lab descriptions. The accepted papers cover the state of the art in information retrieval focusing on user aspects, system and foundational aspects, machine learning, applications, evaluation, new social and technical challenges, and other topics of direct or indirect relevance to search. |
| few-shot learning with retrieval augmented language models: Breaking Barriers with Generative Intelligence. Using GI to Improve Human Education and Well-Being Azza Basiouni, |
| few-shot learning with retrieval augmented language models: Artificial Intelligence in HCI Helmut Degen, |
| few-shot learning with retrieval augmented language models: Understanding Machine Understanding Ken Clements, 2024-10-15 This is a comprehensive and thought-provoking exploration of the nature of machine understanding, its evaluation, and its implications. The book proposes a new framework, the Multifaceted Understanding Test Tool (MUTT), for assessing machine understanding across multiple dimensions, from language comprehension and logical reasoning to social intelligence and metacognition. Through a combination of philosophical analysis, technical exposition, and narrative thought experiments, the book delves into the frontiers of machine understanding, raising fundamental questions about the cognitive mechanisms and representations that enable genuine understanding in both human and machine minds. By probing the boundaries of artificial comprehension, the book aims to advance our theoretical grasp on the elusive notion of understanding and inform responsible development and deployment of AI technologies. In an era where Artificial Intelligence systems are becoming integral to our daily lives, a pressing question arises: Do these machines truly understand what they are doing, or are they merely sophisticated pattern matchers? Understanding Machine Understanding delves into this profound inquiry, exploring the depths of machine cognition and the essence of comprehension. Join Ken Clements and Claude 3 Opus on an intellectual journey that challenges conventional benchmarks like the Turing Test and introduces the innovative Multifaceted Understanding Test Tool (MUTT). This groundbreaking framework assesses AI's capabilities across language, reasoning, perception, and social intelligence, aiming to distinguish genuine understanding from mere imitation. Through philosophical analysis, technical exposition, and engaging narratives, this book invites readers to explore the frontiers of AI comprehension. Whether you're an AI researcher, philosopher, or curious observer, Understanding Machine Understanding offers a thought-provoking guide to the future of human-machine collaboration. Discover what it truly means for a machine to understand--and the implications for our shared future. |
| few-shot learning with retrieval augmented language models: Architecting Data and Machine Learning Platforms Marco Tranquillin, Valliappa Lakshmanan, Firat Tekiner, 2023-10-12 All cloud architects need to know how to build data platforms that enable businesses to make data-driven decisions and deliver enterprise-wide intelligence in a fast and efficient way. This handbook shows you how to design, build, and modernize cloud native data and machine learning platforms using AWS, Azure, Google Cloud, and multicloud tools like Snowflake and Databricks. Authors Marco Tranquillin, Valliappa Lakshmanan, and Firat Tekiner cover the entire data lifecycle from ingestion to activation in a cloud environment using real-world enterprise architectures. You'll learn how to transform, secure, and modernize familiar solutions like data warehouses and data lakes, and you'll be able to leverage recent AI/ML patterns to get accurate and quicker insights to drive competitive advantage. You'll learn how to: Design a modern and secure cloud native or hybrid data analytics and machine learning platform Accelerate data-led innovation by consolidating enterprise data in a governed, scalable, and resilient data platform Democratize access to enterprise data and govern how business teams extract insights and build AI/ML capabilities Enable your business to make decisions in real time using streaming pipelines Build an MLOps platform to move to a predictive and prescriptive analytics approach |
| few-shot learning with retrieval augmented language models: Natural Language Processing and Information Systems Amon Rapp, |
FEW Definition & Meaning - Merriam-Webster
Not many people came, but the few people who did enjoyed themselves. Noun a few of the songs on the album are good, but most are forgettable
FEW | English meaning - Cambridge Dictionary
(A) little and (a) few are quantifiers meaning ‘some’. Little and few have negative meanings. We use them to mean ‘not as much as may be expected or wished for’. …
Few, A Few—What's the Difference? - Grammarly
Few is a quantifier used with plural countable nouns. Without the article “a,” few emphasizes a small number of something. Adding the article removes the emphasis—a few means some. …
few - Wiktionary, the free dictionary
Jun 3, 2025 · In other words, few in this context means a very very small percentage but far more than the 3 or 4 usually ascribed to it in its use with much much smaller numbers. Few is …
FEW definition and meaning | Collins English Dictionary
You use few to indicate that you are talking about a small number of people or things. You can use 'so', 'too', and 'very' in front of few.
Few - definition of few by The Free Dictionary
Few and a few are both used in front of nouns, but they do not have the same meaning. You use a few simply to show that you are talking about a small number of people or things.
Few - Definition, Meaning & Synonyms - Vocabulary.com
Few is a word for a small, non-specific number. A few is somewhere between a couple and a whole bunch. When you say you're going to have a few fries, you'd better not eat the whole …
few pronoun - Definition, pictures, pronunciation and usage notes ...
Definition of few pronoun in Oxford Advanced Learner's Dictionary. Meaning, pronunciation, picture, example sentences, grammar, usage notes, synonyms and more.
FEW Definition & Meaning | Dictionary.com
Few definition: not many but more than one.. See examples of FEW used in a sentence.
What is the difference between few and a few? - Collins Education
Jun 11, 2025 · Few and a few are both used in front of nouns, but they do not have the same meaning. You use a few simply to show that you are talking about a small number of people or …
FEW Definition & Meaning - Merriam-Webster
Not many people came, but the few people who did enjoyed themselves. Noun a few of the songs on the album are good, but most are forgettable
FEW | English meaning - Cambridge Dictionary
(A) little and (a) few are quantifiers meaning ‘some’. Little and few have negative meanings. We use them to mean ‘not as much as may be expected or wished for’. …
Few, A Few—What's the Difference? - Grammarly
Few is a quantifier used with plural countable nouns. Without the article “a,” few emphasizes a small number of something. Adding the article removes the emphasis—a few means some. …
few - Wiktionary, the free dictionary
Jun 3, 2025 · In other words, few in this context means a very very small percentage but far more than the 3 or 4 usually ascribed to it in its use with much much smaller numbers. Few is …
FEW definition and meaning | Collins English Dictionary
You use few to indicate that you are talking about a small number of people or things. You can use 'so', 'too', and 'very' in front of few.
Few - definition of few by The Free Dictionary
Few and a few are both used in front of nouns, but they do not have the same meaning. You use a few simply to show that you are talking about a small number of people or things.
Few - Definition, Meaning & Synonyms - Vocabulary.com
Few is a word for a small, non-specific number. A few is somewhere between a couple and a whole bunch. When you say you're going to have a few fries, you'd better not eat the whole …
few pronoun - Definition, pictures, pronunciation and usage notes ...
Definition of few pronoun in Oxford Advanced Learner's Dictionary. Meaning, pronunciation, picture, example sentences, grammar, usage notes, synonyms and more.
FEW Definition & Meaning | Dictionary.com
Few definition: not many but more than one.. See examples of FEW used in a sentence.
What is the difference between few and a few? - Collins Education
Jun 11, 2025 · Few and a few are both used in front of nouns, but they do not have the same meaning. You use a few simply to show that you are talking about a small number of people or …
