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| differentially private fine-tuning of language models: Large Language Models in Cybersecurity Andrei Kucharavy, 2024 This open access book provides cybersecurity practitioners with the knowledge needed to understand the risks of the increased availability of powerful large language models (LLMs) and how they can be mitigated. It attempts to outrun the malicious attackers by anticipating what they could do. It also alerts LLM developers to understand their work's risks for cybersecurity and provides them with tools to mitigate those risks. The book starts in Part I with a general introduction to LLMs and their main application areas. Part II collects a description of the most salient threats LLMs represent in cybersecurity, be they as tools for cybercriminals or as novel attack surfaces if integrated into existing software. Part III focuses on attempting to forecast the exposure and the development of technologies and science underpinning LLMs, as well as macro levers available to regulators to further cybersecurity in the age of LLMs. Eventually, in Part IV, mitigation techniques that should allowsafe and secure development and deployment of LLMs are presented. The book concludes with two final chapters in Part V, one speculating what a secure design and integration of LLMs from first principles would look like and the other presenting a summary of the duality of LLMs in cyber-security. This book represents the second in a series published by the Technology Monitoring (TM) team of the Cyber-Defence Campus. The first book entitled Trends in Data Protection and Encryption Technologies appeared in 2023. This book series provides technology and trend anticipation for government, industry, and academic decision-makers as well as technical experts. |
| differentially private fine-tuning of language models: Document Analysis and Recognition - ICDAR 2024 Elisa H. Barney Smith, |
| differentially private fine-tuning of language models: Knowledge Management and Acquisition for Intelligent Systems Shiqing Wu, |
| differentially private fine-tuning of language models: Data Mining and Big Data Ying Tan, |
| differentially private fine-tuning of language models: Machine Learning and Knowledge Extraction Andreas Holzinger, Peter Kieseberg, Federico Cabitza, Andrea Campagner, A Min Tjoa, Edgar Weippl, 2023-08-21 This volume LNCS-IFIP constitutes the refereed proceedings of the 7th IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 International Cross-Domain Conference, CD-MAKE 2023 in Benevento, Italy, during August 28 – September 1, 2023. The 18 full papers presented together were carefully reviewed and selected from 30 submissions. The conference focuses on integrative machine learning approach, considering the importance of data science and visualization for the algorithmic pipeline with a strong emphasis on privacy, data protection, safety and security. |
| differentially private fine-tuning of language models: Information Technology Security Debasis Gountia, |
| differentially private fine-tuning of language models: Practicing Trustworthy Machine Learning Yada Pruksachatkun, Matthew Mcateer, Subho Majumdar, 2023-01-03 With the increasing use of AI in high-stakes domains such as medicine, law, and defense, organizations spend a lot of time and money to make ML models trustworthy. Many books on the subject offer deep dives into theories and concepts. This guide provides a practical starting point to help development teams produce models that are secure, more robust, less biased, and more explainable. Authors Yada Pruksachatkun, Matthew McAteer, and Subhabrata Majumdar translate best practices in the academic literature for curating datasets and building models into a blueprint for building industry-grade trusted ML systems. With this book, engineers and data scientists will gain a much-needed foundation for releasing trustworthy ML applications into a noisy, messy, and often hostile world. You'll learn: Methods to explain ML models and their outputs to stakeholders How to recognize and fix fairness concerns and privacy leaks in an ML pipeline How to develop ML systems that are robust and secure against malicious attacks Important systemic considerations, like how to manage trust debt and which ML obstacles require human intervention |
| differentially private fine-tuning of language models: The Algorithmic Foundations of Differential Privacy Cynthia Dwork, Aaron Roth, 2014 The problem of privacy-preserving data analysis has a long history spanning multiple disciplines. As electronic data about individuals becomes increasingly detailed, and as technology enables ever more powerful collection and curation of these data, the need increases for a robust, meaningful, and mathematically rigorous definition of privacy, together with a computationally rich class of algorithms that satisfy this definition. Differential Privacy is such a definition. The Algorithmic Foundations of Differential Privacy starts out by motivating and discussing the meaning of differential privacy, and proceeds to explore the fundamental techniques for achieving differential privacy, and the application of these techniques in creative combinations, using the query-release problem as an ongoing example. A key point is that, by rethinking the computational goal, one can often obtain far better results than would be achieved by methodically replacing each step of a non-private computation with a differentially private implementation. Despite some powerful computational results, there are still fundamental limitations. Virtually all the algorithms discussed herein maintain differential privacy against adversaries of arbitrary computational power -- certain algorithms are computationally intensive, others are efficient. Computational complexity for the adversary and the algorithm are both discussed. The monograph then turns from fundamentals to applications other than query-release, discussing differentially private methods for mechanism design and machine learning. The vast majority of the literature on differentially private algorithms considers a single, static, database that is subject to many analyses. Differential privacy in other models, including distributed databases and computations on data streams, is discussed. The Algorithmic Foundations of Differential Privacy is meant as a thorough introduction to the problems and techniques of differential privacy, and is an invaluable reference for anyone with an interest in the topic. |
| differentially private fine-tuning of language models: Computational Linguistics and Intelligent Text Processing Alexander Gelbukh, 2023-02-25 The two-volume set LNCS 13451 and 13452 constitutes revised selected papers from the CICLing 2019 conference which took place in La Rochelle, France, April 2019. The total of 95 papers presented in the two volumes was carefully reviewed and selected from 335 submissions. The book also contains 3 invited papers. The papers are organized in the following topical sections: General, Information extraction, Information retrieval, Language modeling, Lexical resources, Machine translation, Morphology, sintax, parsing, Name entity recognition, Semantics and text similarity, Sentiment analysis, Speech processing, Text categorization, Text generation, and Text mining. |
| differentially private fine-tuning of language models: Data Security and Privacy Protection Xiaofeng Chen, |
| differentially private fine-tuning of language models: Handbook of Trustworthy Federated Learning My T. Thai, |
| differentially private fine-tuning of language models: Privacy-Preserving Machine Learning Srinivasa Rao Aravilli, 2024-05-24 Gain hands-on experience in data privacy and privacy-preserving machine learning with open-source ML frameworks, while exploring techniques and algorithms to protect sensitive data from privacy breaches Key Features Understand machine learning privacy risks and employ machine learning algorithms to safeguard data against breaches Develop and deploy privacy-preserving ML pipelines using open-source frameworks Gain insights into confidential computing and its role in countering memory-based data attacks Purchase of the print or Kindle book includes a free PDF eBook Book Description– In an era of evolving privacy regulations, compliance is mandatory for every enterprise – Machine learning engineers face the dual challenge of analyzing vast amounts of data for insights while protecting sensitive information – This book addresses the complexities arising from large data volumes and the scarcity of in-depth privacy-preserving machine learning expertise, and covers a comprehensive range of topics from data privacy and machine learning privacy threats to real-world privacy-preserving cases – As you progress, you’ll be guided through developing anti-money laundering solutions using federated learning and differential privacy – Dedicated sections will explore data in-memory attacks and strategies for safeguarding data and ML models – You’ll also explore the imperative nature of confidential computation and privacy-preserving machine learning benchmarks, as well as frontier research in the field – Upon completion, you’ll possess a thorough understanding of privacy-preserving machine learning, equipping them to effectively shield data from real-world threats and attacks What you will learn Study data privacy, threats, and attacks across different machine learning phases Explore Uber and Apple cases for applying differential privacy and enhancing data security Discover IID and non-IID data sets as well as data categories Use open-source tools for federated learning (FL) and explore FL algorithms and benchmarks Understand secure multiparty computation with PSI for large data Get up to speed with confidential computation and find out how it helps data in memory attacks Who this book is for – This comprehensive guide is for data scientists, machine learning engineers, and privacy engineers – Prerequisites include a working knowledge of mathematics and basic familiarity with at least one ML framework (TensorFlow, PyTorch, or scikit-learn) – Practical examples will help you elevate your expertise in privacy-preserving machine learning techniques |
| differentially private fine-tuning of language models: Natural Language Processing and Information Systems Elisabeth Métais, Farid Meziane, Helmut Horacek, Epaminondas Kapetanios, 2021-06-19 This book constitutes the refereed proceedings of the 26th International Conference on Applications of Natural Language to Information Systems, NLDB 2021, held online in July 2021. The 19 full papers and 14 short papers were carefully reviewed and selected from 82 submissions. The papers are organized in the following topical sections: role of learning; methodological approaches; semantic relations; classification; sentiment analysis; social media; linking documents; multimodality; applications. |
| differentially private fine-tuning of language models: GENERATIVE AI INNOVATIONS: Exploring Advanced Techniques and Applications in Modern AI DR. A. SEENU, DR. P. R. SUDHA RANI, |
| differentially private fine-tuning of language models: ChatGPT eBook GURMEET SINGH DANG, |
| differentially private fine-tuning of 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. |
| differentially private fine-tuning of language models: Neural Approaches to Conversational AI: Question Answering, Task-Oriented Dialogues and Social Chatbots Jianfeng Gao, Michel Galley, Lihong Li, 2019-02-21 This monograph is the first survey of neural approaches to conversational AI that targets Natural Language Processing and Information Retrieval audiences. It provides a comprehensive survey of the neural approaches to conversational AI that have been developed in the last few years, covering QA, task-oriented and social bots with a unified view of optimal decision making.The authors draw connections between modern neural approaches and traditional approaches, allowing readers to better understand why and how the research has evolved and to shed light on how they can move forward. They also present state-of-the-art approaches to training dialogue agents using both supervised and reinforcement learning. Finally, the authors sketch out the landscape of conversational systems developed in the research community and released in industry, demonstrating via case studies the progress that has been made and the challenges that are still being faced.Neural Approaches to Conversational AI is a valuable resource for students, researchers, and software developers. It provides a unified view, as well as a detailed presentation of the important ideas and insights needed to understand and create modern dialogue agents that will be instrumental to making world knowledge and services accessible to millions of users in ways that seem natural and intuitive. |
| differentially private fine-tuning of language models: Federated Learning Qiang Yang, Lixin Fan, Han Yu, 2020-11-25 This book provides a comprehensive and self-contained introduction to federated learning, ranging from the basic knowledge and theories to various key applications. Privacy and incentive issues are the focus of this book. It is timely as federated learning is becoming popular after the release of the General Data Protection Regulation (GDPR). Since federated learning aims to enable a machine model to be collaboratively trained without each party exposing private data to others. This setting adheres to regulatory requirements of data privacy protection such as GDPR. This book contains three main parts. Firstly, it introduces different privacy-preserving methods for protecting a federated learning model against different types of attacks such as data leakage and/or data poisoning. Secondly, the book presents incentive mechanisms which aim to encourage individuals to participate in the federated learning ecosystems. Last but not least, this book also describes how federated learning can be applied in industry and business to address data silo and privacy-preserving problems. The book is intended for readers from both the academia and the industry, who would like to learn about federated learning, practice its implementation, and apply it in their own business. Readers are expected to have some basic understanding of linear algebra, calculus, and neural network. Additionally, domain knowledge in FinTech and marketing would be helpful.” |
| differentially private fine-tuning of language models: Text, Speech, and Dialogue Kamil Ekštein, František Pártl, Miloslav Konopík, 2023-08-22 This book constitutes the refereed proceedings of the 26th International Conference on Text, Speech, and Dialogue, TSD 2023, held in Pilsen, Czech Republic, during September 4–6, 2023. The 31 full papers presented together with the abstracts of 3 keynote talks were carefully reviewed and selected from 64 submissions. The conference attracts researchers not only from Central and Eastern Europe but also from other parts of the world. One of its goals has always been bringing together NLP researchers with various interests from different parts of the world and promoting their cooperation. One of the ambitions of the conference is, not only to deal with dialogue systems but also to improve dialogue among researchers in areas of NLP, i.e., among the “text” and the “speech” and the “dialogue” people. |
| differentially private fine-tuning of language models: Deep Generative Models Anirban Mukhopadhyay, |
| differentially private fine-tuning of language models: Prompt Engineering for Large Language Models Nimrita Koul, This eBook ‘Prompt Engineering for Large Language Models’ is meant to be a concise and practical guide for the reader. It teaches you to write better prompts for generative artificial intelligence models like Google’s BARD and OpenAI’s ChatGPT. These models have been trained on huge volumes of data to generate text and provide a free of cost, web-based interface to the underlying models as of 11 Nov. 2023. These models are fine tuned for conversational AI applications. All the prompts used in the eBook have been tested on the web interface of BARD and ChatGPT-3.5. |
| differentially private fine-tuning of language models: The Phonology of Tone and Intonation Carlos Gussenhoven, 2004-07 Publisher Description |
| differentially private fine-tuning of language models: Advanced Applications of Generative AI and Natural Language Processing Models Obaid, Ahmed J., Bhushan, Bharat, S., Muthmainnah, Rajest, S. Suman, 2023-12-21 The rapid advancements in Artificial Intelligence (AI), specifically in Natural Language Processing (NLP) and Generative AI, pose a challenge for academic scholars. Staying current with the latest techniques and applications in these fields is difficult due to their dynamic nature, while the lack of comprehensive resources hinders scholars' ability to effectively utilize these technologies. Advanced Applications of Generative AI and Natural Language Processing Models offers an effective solution to address these challenges. This comprehensive book delves into cutting-edge developments in NLP and Generative AI. It provides insights into the functioning of these technologies, their benefits, and associated challenges. Targeting students, researchers, and professionals in AI, NLP, and computer science, this book serves as a vital reference for deepening knowledge of advanced NLP techniques and staying updated on the latest advancements in generative AI. By providing real-world examples and practical applications, scholars can apply their learnings to solve complex problems across various domains. Embracing Advanced Applications of Generative AI and Natural Language Processing Modelsequips academic scholars with the necessary knowledge and insights to explore innovative applications and unleash the full potential of generative AI and NLP models for effective problem-solving. |
| differentially private fine-tuning of language models: Empirical Inference Bernhard Schölkopf, Zhiyuan Luo, Vladimir Vovk, 2013-12-11 This book honours the outstanding contributions of Vladimir Vapnik, a rare example of a scientist for whom the following statements hold true simultaneously: his work led to the inception of a new field of research, the theory of statistical learning and empirical inference; he has lived to see the field blossom; and he is still as active as ever. He started analyzing learning algorithms in the 1960s and he invented the first version of the generalized portrait algorithm. He later developed one of the most successful methods in machine learning, the support vector machine (SVM) – more than just an algorithm, this was a new approach to learning problems, pioneering the use of functional analysis and convex optimization in machine learning. Part I of this book contains three chapters describing and witnessing some of Vladimir Vapnik's contributions to science. In the first chapter, Léon Bottou discusses the seminal paper published in 1968 by Vapnik and Chervonenkis that lay the foundations of statistical learning theory, and the second chapter is an English-language translation of that original paper. In the third chapter, Alexey Chervonenkis presents a first-hand account of the early history of SVMs and valuable insights into the first steps in the development of the SVM in the framework of the generalised portrait method. The remaining chapters, by leading scientists in domains such as statistics, theoretical computer science, and mathematics, address substantial topics in the theory and practice of statistical learning theory, including SVMs and other kernel-based methods, boosting, PAC-Bayesian theory, online and transductive learning, loss functions, learnable function classes, notions of complexity for function classes, multitask learning, and hypothesis selection. These contributions include historical and context notes, short surveys, and comments on future research directions. This book will be of interest to researchers, engineers, and graduate students engaged with all aspects of statistical learning. |
| differentially private fine-tuning of language models: Speech & Language Processing Dan Jurafsky, 2000-09 |
| differentially private fine-tuning of language models: Differential Privacy and Applications Tianqing Zhu, Gang Li, Wanlei Zhou, Philip S. Yu, 2017-08-22 This book focuses on differential privacy and its application with an emphasis on technical and application aspects. This book also presents the most recent research on differential privacy with a theory perspective. It provides an approachable strategy for researchers and engineers to implement differential privacy in real world applications. Early chapters are focused on two major directions, differentially private data publishing and differentially private data analysis. Data publishing focuses on how to modify the original dataset or the queries with the guarantee of differential privacy. Privacy data analysis concentrates on how to modify the data analysis algorithm to satisfy differential privacy, while retaining a high mining accuracy. The authors also introduce several applications in real world applications, including recommender systems and location privacy Advanced level students in computer science and engineering, as well as researchers and professionals working in privacy preserving, data mining, machine learning and data analysis will find this book useful as a reference. Engineers in database, network security, social networks and web services will also find this book useful. |
| differentially private fine-tuning of language models: Programming Large Language Models with Azure Open AI Francesco Esposito, 2024-04-03 Use LLMs to build better business software applications Autonomously communicate with users and optimize business tasks with applications built to make the interaction between humans and computers smooth and natural. Artificial Intelligence expert Francesco Esposito illustrates several scenarios for which a LLM is effective: crafting sophisticated business solutions, shortening the gap between humans and software-equipped machines, and building powerful reasoning engines. Insight into prompting and conversational programming—with specific techniques for patterns and frameworks—unlock how natural language can also lead to a new, advanced approach to coding. Concrete end-to-end demonstrations (featuring Python and ASP.NET Core) showcase versatile patterns of interaction between existing processes, APIs, data, and human input. Artificial Intelligence expert Francesco Esposito helps you: Understand the history of large language models and conversational programming Apply prompting as a new way of coding Learn core prompting techniques and fundamental use-cases Engineer advanced prompts, including connecting LLMs to data and function calling to build reasoning engines Use natural language in code to define workflows and orchestrate existing APIs Master external LLM frameworks Evaluate responsible AI security, privacy, and accuracy concerns Explore the AI regulatory landscape Build and implement a personal assistant Apply a retrieval augmented generation (RAG) pattern to formulate responses based on a knowledge base Construct a conversational user interface For IT Professionals and Consultants For software professionals, architects, lead developers, programmers, and Machine Learning enthusiasts For anyone else interested in natural language processing or real-world applications of human-like language in software |
| differentially private fine-tuning of language models: 2018 IEEE Symposium on Security and Privacy IEEE Symposium on Security and Privacy, 2018 |
| differentially private fine-tuning of language models: #MeToo and the Politics of Social Change Bianca Fileborn, Rachel Loney-Howes, 2019-09-16 #MeToo has sparked a global re-emergence of sexual violence activism and politics. This edited collection uses the #MeToo movement as a starting point for interrogating contemporary debates in anti-sexual violence activism and justice-seeking. It draws together 19 accessible chapters from academics, practitioners, and sexual violence activists across the globe to provide diverse, critical, and nuanced perspectives on the broader implications of the movement. It taps into wider conversations about the nature, history, and complexities of anti-rape and anti-sexual harassment politics, including the limitations of the movement including in the global South. It features both internationally recognised and emerging academics from across the fields of criminology, media and communications, film studies, gender and queer studies, and law and will appeal broadly to the academic community, activists, and beyond. |
| differentially private fine-tuning of language models: Platform and Model Design for Responsible AI Amita Kapoor, Sharmistha Chatterjee, 2023-04-28 Craft ethical AI projects with privacy, fairness, and risk assessment features for scalable and distributed systems while maintaining explainability and sustainability Purchase of the print or Kindle book includes a free PDF eBook Key Features Learn risk assessment for machine learning frameworks in a global landscape Discover patterns for next-generation AI ecosystems for successful product design Make explainable predictions for privacy and fairness-enabled ML training Book Description AI algorithms are ubiquitous and used for tasks, from recruiting to deciding who will get a loan. With such widespread use of AI in the decision-making process, it's necessary to build an explainable, responsible, transparent, and trustworthy AI-enabled system. With Platform and Model Design for Responsible AI, you'll be able to make existing black box models transparent. You'll be able to identify and eliminate bias in your models, deal with uncertainty arising from both data and model limitations, and provide a responsible AI solution. You'll start by designing ethical models for traditional and deep learning ML models, as well as deploying them in a sustainable production setup. After that, you'll learn how to set up data pipelines, validate datasets, and set up component microservices in a secure and private way in any cloud-agnostic framework. You'll then build a fair and private ML model with proper constraints, tune the hyperparameters, and evaluate the model metrics. By the end of this book, you'll know the best practices to comply with data privacy and ethics laws, in addition to the techniques needed for data anonymization. You'll be able to develop models with explainability, store them in feature stores, and handle uncertainty in model predictions. What you will learn Understand the threats and risks involved in ML models Discover varying levels of risk mitigation strategies and risk tiering tools Apply traditional and deep learning optimization techniques efficiently Build auditable and interpretable ML models and feature stores Understand the concept of uncertainty and explore model explainability tools Develop models for different clouds including AWS, Azure, and GCP Explore ML orchestration tools such as Kubeflow and Vertex AI Incorporate privacy and fairness in ML models from design to deployment Who this book is for This book is for experienced machine learning professionals looking to understand the risks and leakages of ML models and frameworks, and learn to develop and use reusable components to reduce effort and cost in setting up and maintaining the AI ecosystem. |
| differentially private fine-tuning of language models: Interpretable Machine Learning Christoph Molnar, 2020 This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project. |
| differentially private fine-tuning of language models: Privacy and Security Policies in Big Data Tamane, Sharvari, Solanki, Vijender Kumar, Dey, Nilanjan, 2017-03-03 In recent years, technological advances have led to significant developments within a variety of business applications. In particular, data-driven research provides ample opportunity for enterprise growth, if utilized efficiently. Privacy and Security Policies in Big Data is a pivotal reference source for the latest research on innovative concepts on the management of security and privacy analytics within big data. Featuring extensive coverage on relevant areas such as kinetic knowledge, cognitive analytics, and parallel computing, this publication is an ideal resource for professionals, researchers, academicians, advanced-level students, and technology developers in the field of big data. |
| differentially private fine-tuning of language models: Algorithms and Architectures for Parallel Processing Zahir Tari, |
| differentially private fine-tuning of language models: Theory and Applications of Models of Computation , 2008 |
| differentially private fine-tuning of language models: Low-Rank Models in Visual Analysis Zhouchen Lin, Hongyang Zhang, 2017-06-06 Low-Rank Models in Visual Analysis: Theories, Algorithms, and Applications presents the state-of-the-art on low-rank models and their application to visual analysis. It provides insight into the ideas behind the models and their algorithms, giving details of their formulation and deduction. The main applications included are video denoising, background modeling, image alignment and rectification, motion segmentation, image segmentation and image saliency detection. Readers will learn which Low-rank models are highly useful in practice (both linear and nonlinear models), how to solve low-rank models efficiently, and how to apply low-rank models to real problems. - Presents a self-contained, up-to-date introduction that covers underlying theory, algorithms and the state-of-the-art in current applications - Provides a full and clear explanation of the theory behind the models - Includes detailed proofs in the appendices |
| differentially private fine-tuning of language models: Introduction to Information Retrieval Christopher D. Manning, Prabhakar Raghavan, Hinrich Schütze, 2008-07-07 Class-tested and coherent, this textbook teaches classical and web information retrieval, including web search and the related areas of text classification and text clustering from basic concepts. It gives an up-to-date treatment of all aspects of the design and implementation of systems for gathering, indexing, and searching documents; methods for evaluating systems; and an introduction to the use of machine learning methods on text collections. All the important ideas are explained using examples and figures, making it perfect for introductory courses in information retrieval for advanced undergraduates and graduate students in computer science. Based on feedback from extensive classroom experience, the book has been carefully structured in order to make teaching more natural and effective. Slides and additional exercises (with solutions for lecturers) are also available through the book's supporting website to help course instructors prepare their lectures. |
| differentially private fine-tuning of language models: Handbook on Using Administrative Data for Research and Evidence-based Policy Shawn Cole, Iqbal Dhaliwal, Anja Sautmann, 2021 This Handbook intends to inform Data Providers and researchers on how to provide privacy-protected access to, handle, and analyze administrative data, and to link them with existing resources, such as a database of data use agreements (DUA) and templates. Available publicly, the Handbook will provide guidance on data access requirements and procedures, data privacy, data security, property rights, regulations for public data use, data architecture, data use and storage, cost structure and recovery, ethics and privacy-protection, making data accessible for research, and dissemination for restricted access use. The knowledge base will serve as a resource for all researchers looking to work with administrative data and for Data Providers looking to make such data available. |
| differentially private fine-tuning of language models: Optimization for Machine Learning Suvrit Sra, Sebastian Nowozin, Stephen J. Wright, 2012 An up-to-date account of the interplay between optimization and machine learning, accessible to students and researchers in both communities. The interplay between optimization and machine learning is one of the most important developments in modern computational science. Optimization formulations and methods are proving to be vital in designing algorithms to extract essential knowledge from huge volumes of data. Machine learning, however, is not simply a consumer of optimization technology but a rapidly evolving field that is itself generating new optimization ideas. This book captures the state of the art of the interaction between optimization and machine learning in a way that is accessible to researchers in both fields. Optimization approaches have enjoyed prominence in machine learning because of their wide applicability and attractive theoretical properties. The increasing complexity, size, and variety of today's machine learning models call for the reassessment of existing assumptions. This book starts the process of reassessment. It describes the resurgence in novel contexts of established frameworks such as first-order methods, stochastic approximations, convex relaxations, interior-point methods, and proximal methods. It also devotes attention to newer themes such as regularized optimization, robust optimization, gradient and subgradient methods, splitting techniques, and second-order methods. Many of these techniques draw inspiration from other fields, including operations research, theoretical computer science, and subfields of optimization. The book will enrich the ongoing cross-fertilization between the machine learning community and these other fields, and within the broader optimization community. |
| differentially private fine-tuning of language models: Data-Intensive Text Processing with MapReduce Jimmy Lin, Chris Dyer, 2022-05-31 Our world is being revolutionized by data-driven methods: access to large amounts of data has generated new insights and opened exciting new opportunities in commerce, science, and computing applications. Processing the enormous quantities of data necessary for these advances requires large clusters, making distributed computing paradigms more crucial than ever. MapReduce is a programming model for expressing distributed computations on massive datasets and an execution framework for large-scale data processing on clusters of commodity servers. The programming model provides an easy-to-understand abstraction for designing scalable algorithms, while the execution framework transparently handles many system-level details, ranging from scheduling to synchronization to fault tolerance. This book focuses on MapReduce algorithm design, with an emphasis on text processing algorithms common in natural language processing, information retrieval, and machine learning. We introduce the notion of MapReduce design patterns, which represent general reusable solutions to commonly occurring problems across a variety of problem domains. This book not only intends to help the reader think in MapReduce, but also discusses limitations of the programming model as well. Table of Contents: Introduction / MapReduce Basics / MapReduce Algorithm Design / Inverted Indexing for Text Retrieval / Graph Algorithms / EM Algorithms for Text Processing / Closing Remarks |
| differentially private fine-tuning of language models: Deep Learning with Python Francois Chollet, 2017-11-30 Summary Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Machine learning has made remarkable progress in recent years. We went from near-unusable speech and image recognition, to near-human accuracy. We went from machines that couldn't beat a serious Go player, to defeating a world champion. Behind this progress is deep learning—a combination of engineering advances, best practices, and theory that enables a wealth of previously impossible smart applications. About the Book Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples. You'll explore challenging concepts and practice with applications in computer vision, natural-language processing, and generative models. By the time you finish, you'll have the knowledge and hands-on skills to apply deep learning in your own projects. What's Inside Deep learning from first principles Setting up your own deep-learning environment Image-classification models Deep learning for text and sequences Neural style transfer, text generation, and image generation About the Reader Readers need intermediate Python skills. No previous experience with Keras, TensorFlow, or machine learning is required. About the Author François Chollet works on deep learning at Google in Mountain View, CA. He is the creator of the Keras deep-learning library, as well as a contributor to the TensorFlow machine-learning framework. He also does deep-learning research, with a focus on computer vision and the application of machine learning to formal reasoning. His papers have been published at major conferences in the field, including the Conference on Computer Vision and Pattern Recognition (CVPR), the Conference and Workshop on Neural Information Processing Systems (NIPS), the International Conference on Learning Representations (ICLR), and others. Table of Contents PART 1 - FUNDAMENTALS OF DEEP LEARNING What is deep learning? Before we begin: the mathematical building blocks of neural networks Getting started with neural networks Fundamentals of machine learning PART 2 - DEEP LEARNING IN PRACTICE Deep learning for computer vision Deep learning for text and sequences Advanced deep-learning best practices Generative deep learning Conclusions appendix A - Installing Keras and its dependencies on Ubuntu appendix B - Running Jupyter notebooks on an EC2 GPU instance |
DifferentiallyPrivateFine-tuningofLanguageModels - arXiv.org
We give simpler, sparser, and faster algorithms for differentially private fine-tuning of large-scale pre-trainedlanguagemodels,whichachievethestate-of-the …
DIFFERENTIALLY PRIVATE FINE-TUNING OF LANGUAGE …
We give simpler, sparser, and faster algorithms for differentially private fine-tuning of large-scale pre-trained language models that achieve the state-of-the-art privacy versus utility tradeoffs on …
Published as a conference paper at ICLR 2022 - OpenReview
We give simpler, sparser, and faster algorithms for differentially private fine- tuning of large-scale pre-trained language models, which achieve the state-of- the-art privacy versus utility tradeoffs …
Fine-Tuning Language Models with Differential Privacy …
In this paper, we introduce ANADP, a novel DP method that adaptively distributes the noise and privacy budget among a language model's parame- ters during ne-tuning, based on their …
DPZero: Private Fine-Tuning of Language Models without …
When Does Differentially Private Learning Not Suffer in High Dimensions. NeurIPS, 2022.
Differentially Private Fine-Tuning of Language Models
Differentially Private SGD 1. Draw a minibatch of datapoints 2. Compute their gradients 3. Clip per-example gradients to an ℓ2 ball 4. Average gradients 5. Add Gaussian noise 6. Take a …
Differentially Private Next-Token Prediction of Large …
Motivated by these observations, we present Private Mixing of Ensemble Distributions (PMixED): a private prediction protocol for next-token prediction that utilizes the inherent stochasticity of …
When Does Differentially Private Learning Not Suffer in High …
Large pretrained models can be fine-tuned with differential privacy to achieve performance approaching that of non-private models. A common theme in these results is the surprising …
Privately Fine-Tuning Large Language Models with Differential …
To address the gap, we present EW-Tune, a DP framework for fine-tuning LLMs based on Edgeworth accountant with finite-sample privacy guarantees.
Differentially Private Language Models Benefit from Public Pre …
We instead train a non-private base model on a large, public dataset, which we proceed to fine-tune on a private out-of-distribution dataset through differentially private stochastic gradient …
Differentially Private Model Compression - University of …
Recent papers have shown that large pre-trained language models (LLMs) such as BERT, GPT-2 can be fine-tuned on private data to achieve performance comparable to non-private models …
Private Fine-tuning of LLMs without Backpropagation
Differentially Private Stochastic Gradient Descent (DP-SGD) was thought to be unfit for large scale optimization. as long as we are fine-tuning a pretrained model. Open question 1: Why …
Improved Algorithms for Differentially Private Language …
We propose a unified framework for privacy-preserving language model alignment, that consists of a sequence of losses minimization. This unified framework includes cur-rent commonly …
Private Fine-tuning of Large Language Models with Zeroth
We introduce DP-ZO, a new 019 method for fine-tuning large language models that 020 preserves the privacy of training data by priva-021 tizing zeroth-order optimization.
Privacy Auditing and Protection in Large Language Models
Memorization in Fine-tuning Large Language Models Fine-tuning (domain adaptation) can be riskier in terms of privacy, as it is more often, on smallerdomain specific datasets, such as …
DifferentiallyPrivateFine-tuningofLanguageModels - arXiv.org
We give simpler, sparser, and faster algorithms for differentially private fine-tuning of large-scale pre-trainedlanguagemodels,whichachievethestate-of-the …
Large Language Models Can Be Strong Differentially Private …
Fine-tuning task formulation matters - For classification, CLS-token fine-tuning introduces a discrepancy between pretraining (masked language modeling) and fine-tuning (network on top …
arXiv:2411.15831v1 [cs.LG] 24 Nov 2024
Nov 26, 2024 · Fine-tuning large language models (LLMs) for specific tasks introduces privacy risks, as models may inadvertently memorise and leak sensitive training data. While …
LMO-DP: ACCURATELY FINE-TUNING LANGUAGE MODELS …
To mitigate privacy risks in deep learning training and fine-tuning, differential privacy (DP) (Dwork (2006)) has been widely recognized as the de facto rigorous privacy model where adding or re …
DPZero: Private Fine-Tuning of Language Models without …
•We verify the effectiveness ofDPZero in both synthetic examples and private fine-tuning tasks on RoBERTa [80] and OPT [148]. In contrast to first-order algorithms that demand extensive …
DifferentiallyPrivateFine-tuningofLanguageModels - arXiv.org
We give simpler, sparser, and faster algorithms for differentially private fine-tuning of large-scale pre-trainedlanguagemodels,whichachievethestate-of-the …
DIFFERENTIALLY PRIVATE FINE-TUNING OF LANGUAGE …
We give simpler, sparser, and faster algorithms for differentially private fine-tuning of large-scale pre-trained language models that achieve the state-of-the-art privacy versus utility tradeoffs on …
Published as a conference paper at ICLR 2022 - OpenReview
We give simpler, sparser, and faster algorithms for differentially private fine- tuning of large-scale pre-trained language models, which achieve the state-of- the-art privacy versus utility tradeoffs …
Fine-Tuning Language Models with Differential Privacy …
In this paper, we introduce ANADP, a novel DP method that adaptively distributes the noise and privacy budget among a language model's parame- ters during ne-tuning, based on their …
DPZero: Private Fine-Tuning of Language Models without …
When Does Differentially Private Learning Not Suffer in High Dimensions. NeurIPS, 2022.
Differentially Private Fine-Tuning of Language Models
Differentially Private SGD 1. Draw a minibatch of datapoints 2. Compute their gradients 3. Clip per-example gradients to an ℓ2 ball 4. Average gradients 5. Add Gaussian noise 6. Take a …
Differentially Private Next-Token Prediction of Large …
Motivated by these observations, we present Private Mixing of Ensemble Distributions (PMixED): a private prediction protocol for next-token prediction that utilizes the inherent stochasticity of …
When Does Differentially Private Learning Not Suffer in …
Large pretrained models can be fine-tuned with differential privacy to achieve performance approaching that of non-private models. A common theme in these results is the surprising …
Privately Fine-Tuning Large Language Models with …
To address the gap, we present EW-Tune, a DP framework for fine-tuning LLMs based on Edgeworth accountant with finite-sample privacy guarantees.
Differentially Private Language Models Benefit from Public …
We instead train a non-private base model on a large, public dataset, which we proceed to fine-tune on a private out-of-distribution dataset through differentially private stochastic gradient …
Differentially Private Model Compression - University of …
Recent papers have shown that large pre-trained language models (LLMs) such as BERT, GPT-2 can be fine-tuned on private data to achieve performance comparable to non-private models …
Private Fine-tuning of LLMs without Backpropagation
Differentially Private Stochastic Gradient Descent (DP-SGD) was thought to be unfit for large scale optimization. as long as we are fine-tuning a pretrained model. Open question 1: Why …
Improved Algorithms for Differentially Private Language …
We propose a unified framework for privacy-preserving language model alignment, that consists of a sequence of losses minimization. This unified framework includes cur-rent commonly …
Private Fine-tuning of Large Language Models with Zeroth …
We introduce DP-ZO, a new 019 method for fine-tuning large language models that 020 preserves the privacy of training data by priva-021 tizing zeroth-order optimization.
Privacy Auditing and Protection in Large Language Models
Memorization in Fine-tuning Large Language Models Fine-tuning (domain adaptation) can be riskier in terms of privacy, as it is more often, on smallerdomain specific datasets, such as …
DifferentiallyPrivateFine-tuningofLanguageModels - arXiv.org
We give simpler, sparser, and faster algorithms for differentially private fine-tuning of large-scale pre-trainedlanguagemodels,whichachievethestate-of-the …
Large Language Models Can Be Strong Differentially …
Fine-tuning task formulation matters - For classification, CLS-token fine-tuning introduces a discrepancy between pretraining (masked language modeling) and fine-tuning (network on top …
arXiv:2411.15831v1 [cs.LG] 24 Nov 2024
Nov 26, 2024 · Fine-tuning large language models (LLMs) for specific tasks introduces privacy risks, as models may inadvertently memorise and leak sensitive training data. While …
LMO-DP: ACCURATELY FINE-TUNING LANGUAGE MODELS …
To mitigate privacy risks in deep learning training and fine-tuning, differential privacy (DP) (Dwork (2006)) has been widely recognized as the de facto rigorous privacy model where adding or re …
DPZero: Private Fine-Tuning of Language Models without …
•We verify the effectiveness ofDPZero in both synthetic examples and private fine-tuning tasks on RoBERTa [80] and OPT [148]. In contrast to first-order algorithms that demand extensive …
