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| causal language modeling vs masked language modeling: 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. |
| causal language modeling vs masked language modeling: Large Language Model-Based Solutions Shreyas Subramanian, 2024-04-02 Learn to build cost-effective apps using Large Language Models In Large Language Model-Based Solutions: How to Deliver Value with Cost-Effective Generative AI Applications, Principal Data Scientist at Amazon Web Services, Shreyas Subramanian, delivers a practical guide for developers and data scientists who wish to build and deploy cost-effective large language model (LLM)-based solutions. In the book, you'll find coverage of a wide range of key topics, including how to select a model, pre- and post-processing of data, prompt engineering, and instruction fine tuning. The author sheds light on techniques for optimizing inference, like model quantization and pruning, as well as different and affordable architectures for typical generative AI (GenAI) applications, including search systems, agent assists, and autonomous agents. You'll also find: Effective strategies to address the challenge of the high computational cost associated with LLMs Assistance with the complexities of building and deploying affordable generative AI apps, including tuning and inference techniques Selection criteria for choosing a model, with particular consideration given to compact, nimble, and domain-specific models Perfect for developers and data scientists interested in deploying foundational models, or business leaders planning to scale out their use of GenAI, Large Language Model-Based Solutions will also benefit project leaders and managers, technical support staff, and administrators with an interest or stake in the subject. |
| causal language modeling vs masked language modeling: Database Systems for Advanced Applications Yunmook Nah, Bin Cui, Sang-Won Lee, Jeffrey Xu Yu, Yang-Sae Moon, Steven Euijong Whang, 2020-09-21 The 4 volume set LNCS 12112-12114 constitutes the papers of the 25th International Conference on Database Systems for Advanced Applications which will be held online in September 2020. The 119 full papers presented together with 19 short papers plus 15 demo papers and 4 industrial papers in this volume were carefully reviewed and selected from a total of 487 submissions. The conference program presents the state-of-the-art R&D activities in database systems and their applications. It provides a forum for technical presentations and discussions among database researchers, developers and users from academia, business and industry. |
| causal language modeling vs masked language modeling: Natural Language Processing with Transformers, Revised Edition Lewis Tunstall, Leandro von Werra, Thomas Wolf, 2022-05-26 Since their introduction in 2017, transformers have quickly become the dominant architecture for achieving state-of-the-art results on a variety of natural language processing tasks. If you're a data scientist or coder, this practical book -now revised in full color- shows you how to train and scale these large models using Hugging Face Transformers, a Python-based deep learning library. Transformers have been used to write realistic news stories, improve Google Search queries, and even create chatbots that tell corny jokes. In this guide, authors Lewis Tunstall, Leandro von Werra, and Thomas Wolf, among the creators of Hugging Face Transformers, use a hands-on approach to teach you how transformers work and how to integrate them in your applications. You'll quickly learn a variety of tasks they can help you solve. Build, debug, and optimize transformer models for core NLP tasks, such as text classification, named entity recognition, and question answering Learn how transformers can be used for cross-lingual transfer learning Apply transformers in real-world scenarios where labeled data is scarce Make transformer models efficient for deployment using techniques such as distillation, pruning, and quantization Train transformers from scratch and learn how to scale to multiple GPUs and distributed environments |
| causal language modeling vs masked language modeling: 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. |
| causal language modeling vs masked language modeling: Introduction to Python and Large Language Models Dilyan Grigorov, |
| causal language modeling vs masked language modeling: Engineering Mathematics and Artificial Intelligence Herb Kunze, Davide La Torre, Adam Riccoboni, Manuel Ruiz Galán, 2023-07-26 Explains the theory behind Machine Learning and highlights how Mathematics can be used in Artificial Intelligence Illustrates how to improve existing algorithms by using advanced mathematics and discusses how Machine Learning can support mathematical modeling Captures how to simulate data by means of artificial neural networks and offers cutting-edge Artificial Intelligence technologies Emphasizes the classification of algorithms, optimization methods, and statistical techniques Explores future integration between Machine Learning and complex mathematical techniques |
| causal language modeling vs masked language modeling: 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 |
| causal language modeling vs masked language modeling: Intelligent Systems Murilo C. Naldi, Reinaldo A. C. Bianchi, 2023-10-11 The three-volume set LNAI 14195, 14196, and 14197 constitutes the refereed proceedings of the 12th Brazilian Conference on Intelligent Systems, BRACIS 2023, which took place in Belo Horizonte, Brazil, in September 2023. The 90 full papers included in the proceedings were carefully reviewed and selected from 242 submissions. They have been organized in topical sections as follows: Part I: Best papers; resource allocation and planning; rules and feature extraction; AI and education; agent systems; explainability; AI models; Part II: Transformer applications; convolutional neural networks; deep learning applications; reinforcement learning and GAN; classification; machine learning analysis; Part III: Evolutionary algorithms; optimization strategies; computer vision; language and models; graph neural networks; pattern recognition; AI applications. |
| causal language modeling vs masked language modeling: A Beginner's Guide to Large Language Models StoryBuddiesPlay, 2024-09-08 A Beginner's Guide to Large Language Models is an essential resource for anyone looking to understand and work with cutting-edge AI language technology. This comprehensive guide covers everything from the basics of natural language processing to advanced topics like model architecture, training techniques, and ethical considerations. Whether you're a student, researcher, or industry professional, this book provides the knowledge and practical insights needed to navigate the exciting world of Large Language Models. Discover how these powerful AI systems are reshaping the landscape of language understanding and generation, and learn how to apply them in real-world scenarios. Large Language Models, AI, Natural Language Processing, Machine Learning, Deep Learning, Transformers, GPT, BERT, Neural Networks, Text Generation |
| causal language modeling vs masked language modeling: Computational Collective Intelligence Ngoc Thanh Nguyen, János Botzheim, László Gulyás, Manuel Núñez, Jan Treur, Gottfried Vossen, Adrianna Kozierkiewicz, 2023-09-12 This book constitutes the refereed proceedings of the 15th International Conference on Computational Collective Intelligence, ICCCI 2023, held in Budapest, Hungary, during September 27–29, 2023. The 63 full papers included in this book were carefully reviewed and selected from 218 submissions. They are organized in topical sections as follows: collective intelligence and collective decision-making; deep learning techniques; natural language processing; data mining and machine learning; social networks and intelligent systems; cybersecurity, blockchain technology and Internet of Things; cooperative strategies for decision making and optimization; computational intelligence for digital content understanding; knowledge engineering and application for Industry 4.0; computational intelligence in medical applications; and ensemble models and data fusion. |
| causal language modeling vs masked language modeling: 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 |
| causal language modeling vs masked language modeling: Applied Natural Language Processing in the Enterprise Ankur A. Patel, Ajay Uppili Arasanipalai, 2021-05-12 NLP has exploded in popularity over the last few years. But while Google, Facebook, OpenAI, and others continue to release larger language models, many teams still struggle with building NLP applications that live up to the hype. This hands-on guide helps you get up to speed on the latest and most promising trends in NLP. With a basic understanding of machine learning and some Python experience, you'll learn how to build, train, and deploy models for real-world applications in your organization. Authors Ankur Patel and Ajay Uppili Arasanipalai guide you through the process using code and examples that highlight the best practices in modern NLP. Use state-of-the-art NLP models such as BERT and GPT-3 to solve NLP tasks such as named entity recognition, text classification, semantic search, and reading comprehension Train NLP models with performance comparable or superior to that of out-of-the-box systems Learn about Transformer architecture and modern tricks like transfer learning that have taken the NLP world by storm Become familiar with the tools of the trade, including spaCy, Hugging Face, and fast.ai Build core parts of the NLP pipeline--including tokenizers, embeddings, and language models--from scratch using Python and PyTorch Take your models out of Jupyter notebooks and learn how to deploy, monitor, and maintain them in production |
| causal language modeling vs masked language modeling: Pattern Recognition. ICPR International Workshops and Challenges Alberto Del Bimbo, Rita Cucchiara, Stan Sclaroff, Giovanni Maria Farinella, Tao Mei, Marco Bertini, Hugo Jair Escalante, Roberto Vezzani, 2021-02-24 This 8-volumes set constitutes the refereed of the 25th International Conference on Pattern Recognition Workshops, ICPR 2020, held virtually in Milan, Italy and rescheduled to January 10 - 11, 2021 due to Covid-19 pandemic. The 416 full papers presented in these 8 volumes were carefully reviewed and selected from about 700 submissions. The 46 workshops cover a wide range of areas including machine learning, pattern analysis, healthcare, human behavior, environment, surveillance, forensics and biometrics, robotics and egovision, cultural heritage and document analysis, retrieval, and women at ICPR2020. |
| causal language modeling vs masked language modeling: Experimental IR Meets Multilinguality, Multimodality, and Interaction Avi Arampatzis, Evangelos Kanoulas, Theodora Tsikrika, Stefanos Vrochidis, Hideo Joho, Christina Lioma, Carsten Eickhoff, Aurélie Névéol, Linda Cappellato, Nicola Ferro, 2020-09-15 This book constitutes the refereed proceedings of the 11th International Conference of the CLEF Association, CLEF 2020, held in Thessaloniki, Greece, in September 2020.* The conference has a clear focus on experimental information retrieval with special attention to the challenges of multimodality, multilinguality, and interactive search ranging from unstructured to semi structures and structured data. The 5 full papers and 2 short papers presented in this volume were carefully reviewed and selected from 9 submissions. This year, the contributions addressed the following challenges: a large-scale evaluation of translation effects in academic search, advancement of assessor-driven aggregation methods for efficient relevance assessments, and development of a new test dataset. In addition to this, the volume presents 7 “best of the labs” papers which were reviewed as full paper submissions with the same review criteria. The 12 lab overview papers were accepted out of 15 submissions and represent scientific challenges based on new data sets and real world problems in multimodal and multilingual information access. * The conference was held virtually due to the COVID-19 pandemic. |
| causal language modeling vs masked language modeling: Service-Oriented Computing Hakim Hacid, Odej Kao, Massimo Mecella, Naouel Moha, Hye-young Paik, 2021-11-17 This book constitutes the proceedings of the 19th International Conference on Service-Oriented Computing, ICSOC 2020, which is held virtually in November 2021. The 29 full, 28 short, and 3 vision papers included in this volume were carefully reviewed and selected from 189 submissions. They were organized in topical sections named: Blockchains and smart contracts, Architectures, microservices and APIs, Applications, Internet-of-Things, crowdsourced, social, and conversational services, Service composition and recommendation, Cloud computing, and Edge computing. |
| causal language modeling vs masked language modeling: Artificial Intelligence and Human Enhancement Herta Nagl-Docekal, Waldemar Zacharasiewicz, 2022-04-04 Seit 2014 erscheinen die Bände der renommierten Wiener Reihe bei De Gruyter. Das äußere Layout der Bände wurde modernisiert, inhaltlich und personell jedoch ist das Profil der seit mehr als zwei Jahrzehnten erscheinenden Buchreihe von Kontinuität geprägt. Die Bände sind jeweils einer aktuellen philosophischen Fragestellung gewidmet. Eine internationale Autorenschaft und die Veröffentlichung fremdsprachiger Beiträge sind Elemente des Programms. Die Reihe will dazu beitragen, dogmatische Abgrenzungen zwischen philosophischen Schulen und Traditionen abzubauen. |
| causal language modeling vs masked language modeling: The Generative AI Practitioner’s Guide Arup Das, David Sweenor, 2024-07-20 Generative AI is revolutionizing the way organizations leverage technology to gain a competitive edge. However, as more companies experiment with and adopt AI systems, it becomes challenging for data and analytics professionals, AI practitioners, executives, technologists, and business leaders to look beyond the buzz and focus on the essential questions: Where should we begin? How do we initiate the process? What potential pitfalls should we be aware of? This TinyTechGuide offers valuable insights and practical recommendations on constructing a business case, calculating ROI, exploring real-life applications, and considering ethical implications. Crucially, it introduces five LLM patterns—author, retriever, extractor, agent, and experimental—to effectively implement GenAI systems within an organization. The Generative AI Practitioner’s Guide: How to Apply LLM Patterns for Enterprise Applications bridges critical knowledge gaps for business leaders and practitioners, equipping them with a comprehensive toolkit to define a business case and successfully deploy GenAI. In today’s rapidly evolving world, staying ahead of the competition requires a deep understanding of these five implementation patterns and the potential benefits and risks associated with GenAI. Designed for business leaders, tech experts, and IT teams, this book provides real-life examples and actionable insights into GenAI’s transformative impact on various industries. Empower your organization with a competitive edge in today’s marketplace using The Generative AI Practitioner’s Guide: How to Apply LLM Patterns for Enterprise Applications. Remember, it’s not the tech that’s tiny, just the book!™ |
| causal language modeling vs masked language modeling: Large Language Models for Natural Language Processing StoryBuddiesPlay, 2024-09-11 Large Language Models for Natural Language Processing: Advanced Techniques is an essential guide for researchers, practitioners, and enthusiasts in the field of artificial intelligence and natural language processing. This comprehensive book delves into the cutting-edge world of Large Language Models, exploring their architecture, training methodologies, and wide-ranging applications. From mastering prompt engineering to understanding ethical considerations, readers will gain in-depth knowledge of LLMs' capabilities in natural language understanding and generation. With insights into emerging trends and future directions, this book equips you with the expertise needed to harness the power of LLMs for revolutionary advancements in AI and NLP. Large Language Models, Natural Language Processing, AI, Machine Learning, Prompt Engineering, Bias Mitigation, Text Generation, Semantic Parsing, Neural Networks, Transformer Architecture |
| causal language modeling vs masked language modeling: Artificial Intelligence For Science: A Deep Learning Revolution Alok Choudhary, Geoffrey C Fox, Tony Hey, 2023-03-21 This unique collection introduces AI, Machine Learning (ML), and deep neural network technologies leading to scientific discovery from the datasets generated both by supercomputer simulation and by modern experimental facilities.Huge quantities of experimental data come from many sources — telescopes, satellites, gene sequencers, accelerators, and electron microscopes, including international facilities such as the Large Hadron Collider (LHC) at CERN in Geneva and the ITER Tokamak in France. These sources generate many petabytes moving to exabytes of data per year. Extracting scientific insights from these data is a major challenge for scientists, for whom the latest AI developments will be essential.The timely handbook benefits professionals, researchers, academics, and students in all fields of science and engineering as well as AI, ML, and neural networks. Further, the vision evident in this book inspires all those who influence or are influenced by scientific progress. |
| causal language modeling vs masked language modeling: Advances in Information Retrieval Djoerd Hiemstra, Marie-Francine Moens, Josiane Mothe, Raffaele Perego, Martin Potthast, Fabrizio Sebastiani, 2021-03-26 This two-volume set LNCS 12656 and 12657 constitutes the refereed proceedings of the 43rd European Conference on IR Research, ECIR 2021, held virtually in March/April 2021, due to the COVID-19 pandemic. The 50 full papers presented together with 11 reproducibility papers, 39 short papers, 15 demonstration papers, 12 CLEF lab descriptions papers, 5 doctoral consortium papers, 5 workshop abstracts, and 8 tutorials abstracts were carefully reviewed and selected from 436 submissions. The accepted contributions cover the state of the art in IR: deep learning-based information retrieval techniques, use of entities and knowledge graphs, recommender systems, retrieval methods, information extraction, question answering, topic and prediction models, multimedia retrieval, and much more. |
| causal language modeling vs masked language modeling: Build a Large Language Model (From Scratch) Sebastian Raschka, 2024-10-29 Learn how to create, train, and tweak large language models (LLMs) by building one from the ground up! In Build a Large Language Model (from Scratch) bestselling author Sebastian Raschka guides you step by step through creating your own LLM. Each stage is explained with clear text, diagrams, and examples. You’ll go from the initial design and creation, to pretraining on a general corpus, and on to fine-tuning for specific tasks. Build a Large Language Model (from Scratch) teaches you how to: • Plan and code all the parts of an LLM • Prepare a dataset suitable for LLM training • Fine-tune LLMs for text classification and with your own data • Use human feedback to ensure your LLM follows instructions • Load pretrained weights into an LLM Build a Large Language Model (from Scratch) takes you inside the AI black box to tinker with the internal systems that power generative AI. As you work through each key stage of LLM creation, you’ll develop an in-depth understanding of how LLMs work, their limitations, and their customization methods. Your LLM can be developed on an ordinary laptop, and used as your own personal assistant. About the technology Physicist Richard P. Feynman reportedly said, “I don’t understand anything I can’t build.” Based on this same powerful principle, bestselling author Sebastian Raschka guides you step by step as you build a GPT-style LLM that you can run on your laptop. This is an engaging book that covers each stage of the process, from planning and coding to training and fine-tuning. About the book Build a Large Language Model (From Scratch) is a practical and eminently-satisfying hands-on journey into the foundations of generative AI. Without relying on any existing LLM libraries, you’ll code a base model, evolve it into a text classifier, and ultimately create a chatbot that can follow your conversational instructions. And you’ll really understand it because you built it yourself! What's inside • Plan and code an LLM comparable to GPT-2 • Load pretrained weights • Construct a complete training pipeline • Fine-tune your LLM for text classification • Develop LLMs that follow human instructions About the reader Readers need intermediate Python skills and some knowledge of machine learning. The LLM you create will run on any modern laptop and can optionally utilize GPUs. About the author Sebastian Raschka is a Staff Research Engineer at Lightning AI, where he works on LLM research and develops open-source software. The technical editor on this book was David Caswell. Table of Contents 1 Understanding large language models 2 Working with text data 3 Coding attention mechanisms 4 Implementing a GPT model from scratch to generate text 5 Pretraining on unlabeled data 6 Fine-tuning for classification 7 Fine-tuning to follow instructions A Introduction to PyTorch B References and further reading C Exercise solutions D Adding bells and whistles to the training loop E Parameter-efficient fine-tuning with LoRA |
| causal language modeling vs masked language modeling: Getting Started with Google BERT Sudharsan Ravichandiran, 2021-01-22 Kickstart your NLP journey by exploring BERT and its variants such as ALBERT, RoBERTa, DistilBERT, VideoBERT, and more with Hugging Face's transformers library Key FeaturesExplore the encoder and decoder of the transformer modelBecome well-versed with BERT along with ALBERT, RoBERTa, and DistilBERTDiscover how to pre-train and fine-tune BERT models for several NLP tasksBook Description BERT (bidirectional encoder representations from transformer) has revolutionized the world of natural language processing (NLP) with promising results. This book is an introductory guide that will help you get to grips with Google's BERT architecture. With a detailed explanation of the transformer architecture, this book will help you understand how the transformer’s encoder and decoder work. You’ll explore the BERT architecture by learning how the BERT model is pre-trained and how to use pre-trained BERT for downstream tasks by fine-tuning it for NLP tasks such as sentiment analysis and text summarization with the Hugging Face transformers library. As you advance, you’ll learn about different variants of BERT such as ALBERT, RoBERTa, and ELECTRA, and look at SpanBERT, which is used for NLP tasks like question answering. You'll also cover simpler and faster BERT variants based on knowledge distillation such as DistilBERT and TinyBERT. The book takes you through MBERT, XLM, and XLM-R in detail and then introduces you to sentence-BERT, which is used for obtaining sentence representation. Finally, you'll discover domain-specific BERT models such as BioBERT and ClinicalBERT, and discover an interesting variant called VideoBERT. By the end of this BERT book, you’ll be well-versed with using BERT and its variants for performing practical NLP tasks. What you will learnUnderstand the transformer model from the ground upFind out how BERT works and pre-train it using masked language model (MLM) and next sentence prediction (NSP) tasksGet hands-on with BERT by learning to generate contextual word and sentence embeddingsFine-tune BERT for downstream tasksGet to grips with ALBERT, RoBERTa, ELECTRA, and SpanBERT modelsGet the hang of the BERT models based on knowledge distillationUnderstand cross-lingual models such as XLM and XLM-RExplore Sentence-BERT, VideoBERT, and BARTWho this book is for This book is for NLP professionals and data scientists looking to simplify NLP tasks to enable efficient language understanding using BERT. A basic understanding of NLP concepts and deep learning is required to get the best out of this book. |
| causal language modeling vs masked language modeling: Computational Intelligence Applications for Text and Sentiment Data Analysis Dipankar Das, Anup Kumar Kolya, Abhishek Basu, Soham Sarkar, 2023-07-14 Approx.330 pagesApprox.330 pages |
| causal language modeling vs masked language modeling: Challenges in Large Language Model Development and AI Ethics Gupta, Brij, 2024-08-15 The development of large language models has resulted in artificial intelligence advancements promising transformations and benefits across various industries and sectors. However, this progress is not without its challenges. The scale and complexity of these models pose significant technical hurdles, including issues related to bias, transparency, and data privacy. As these models integrate into decision-making processes, ethical concerns about their societal impact, such as potential job displacement or harmful stereotype reinforcement, become more urgent. Addressing these challenges requires a collaborative effort from business owners, computer engineers, policymakers, and sociologists. Fostering effective research for solutions to address AI ethical challenges may ensure that large language model developments benefit society in a positive way. Challenges in Large Language Model Development and AI Ethics addresses complex ethical dilemmas and challenges of the development of large language models and artificial intelligence. It analyzes ethical considerations involved in the design and implementation of large language models, while exploring aspects like bias, accountability, privacy, and social impacts. This book covers topics such as law and policy, model architecture, and machine learning, and is a useful resource for computer engineers, sociologists, policymakers, business owners, academicians, researchers, and scientists. |
| causal language modeling vs masked language modeling: Service-Oriented Computing – ICSOC 2021 Workshops Hakim Hacid, Monther Aldwairi, Mohamed Reda Bouadjenek, Marinella Petrocchi, Noura Faci, Fatma Outay, Amin Beheshti, Lauritz Thamsen, Hai Dong, 2022-08-23 This book constitutes the selected papers from the scientific satellite events held in conjunction with the19th International Conference on Service-Oriented Computing, ICSOC 2021. The conference was held Dubai, United Arab Emirates in November 2021. This year, these satellite events were organized around three main tracks, including a workshop track, a demonstration track, and a tutorials track. The ICSOC 2021 workshop track consisted of the following three workshops covering a wide range of topics that fall into the general area of service computing. • International Workshop on Artificial Intelligence for IT Operations (AIOps) • 3rd Workshop on Smart Data Integration and Processing (STRAPS 2021) • International Workshop on AI-enabled Process Automation (AI-PA 2021) |
| causal language modeling vs masked language modeling: Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium and Blue Sky Andrew M. Olney, |
| causal language modeling vs masked language modeling: AI and data science in drug development and public health: Highlights from the MCBIOS 2022 conference Ramin Homayouni, Prashanti Manda, Aik Choon Tan, Zhaohui Steve Qin, 2023-03-27 |
| causal language modeling vs masked language modeling: Recommender Systems in Fashion and Retail Humberto Jesús Corona Pampín, Reza Shirvany, 2023-03-01 This book includes the proceedings of the fourth workshop on recommender systems in fashion and retail (2022), and it aims to present a state-of-the-art view of the advancements within the field of recommendation systems with focused application to e-commerce, retail, and fashion by presenting readers with chapters covering contributions from academic as well as industrial researchers active within this emerging new field. Recommender systems are often used to solve different complex problems in this scenario, such as product recommendations, size and fit recommendations, and social media-influenced recommendations (outfits worn by influencers). |
| causal language modeling vs masked language modeling: Responsible Data Science Jimson Mathew, G. Santhosh Kumar, Deepak P., Joemon M. Jose, 2022-11-14 This book comprises select proceedings of the 7th International Conference on Data Science and Engineering (ICDSE 2021). The contents of this book focus on responsible data science. This book tries to integrate research across diverse topics related to data science, such as fairness, trust, ethics, confidentiality, transparency, and accuracy. The chapters in this book represent research from different perspectives that offer novel theoretical implications that span multiple disciplines. The book will serve as a reference resource for researchers and practitioners in academia and industry. |
| causal language modeling vs masked language modeling: Distributional Semantics Alessandro Lenci, Magnus Sahlgren, 2023-09-30 This book provides a comprehensive foundation of distributional methods in computational modeling of meaning. It aims to build a common understanding of the theoretical and methodological foundations for students of computational linguistics, natural language processing, computer science, artificial intelligence, and cognitive science. |
| causal language modeling vs masked language modeling: ICT Innovations 2022. Reshaping the Future Towards a New Normal Katerina Zdravkova, Lasko Basnarkov, 2023-01-01 This book constitutes the refereed proceedings of the 14th International Conference on ICT Innovations 2022. Reshaping the Future Towards a New Normal, ICT Innovations 2022, held in Skopje, Macedonia, during September 29–October 1, 2022. The 14 full papers and 1 short papers included in this book were carefully reviewed and selected from 42 submissions. They were organized in topical sections as follows: theoretical foundations and distributed computing; artificial intelligence and deep learning; applied artificial intelligence; education; and medical informatics. |
| causal language modeling vs masked language modeling: Large Language Models John Atkinson-Abutridy, 2024-10-17 This book serves as an introduction to the science and applications of Large Language Models (LLMs). You'll discover the common thread that drives some of the most revolutionary recent applications of artificial intelligence (AI): from conversational systems like ChatGPT or BARD, to machine translation, summary generation, question answering, and much more. At the heart of these innovative applications is a powerful and rapidly evolving discipline, natural language processing (NLP). For more than 60 years, research in this science has been focused on enabling machines to efficiently understand and generate human language. The secrets behind these technological advances lie in LLMs, whose power lies in their ability to capture complex patterns and learn contextual representations of language. How do these LLMs work? What are the available models and how are they evaluated? This book will help you answer these and many other questions. With a technical but accessible introduction: •You will explore the fascinating world of LLMs, from its foundations to its most powerful applications •You will learn how to build your own simple applications with some of the LLMs Designed to guide you step by step, with six chapters combining theory and practice, along with exercises in Python on the Colab platform, you will master the secrets of LLMs and their application in NLP. From deep neural networks and attention mechanisms, to the most relevant LLMs such as BERT, GPT-4, LLaMA, Palm-2 and Falcon, this book guides you through the most important achievements in NLP. Not only will you learn the benchmarks used to evaluate the capabilities of these models, but you will also gain the skill to create your own NLP applications. It will be of great value to professionals, researchers and students within AI, data science and beyond. |
| causal language modeling vs masked language modeling: The Machine Learning Solutions Architect Handbook David Ping, 2024-04-15 Design, build, and secure scalable machine learning (ML) systems to solve real-world business problems with Python and AWS Purchase of the print or Kindle book includes a free PDF eBook Key Features Go in-depth into the ML lifecycle, from ideation and data management to deployment and scaling Apply risk management techniques in the ML lifecycle and design architectural patterns for various ML platforms and solutions Understand the generative AI lifecycle, its core technologies, and implementation risks Book DescriptionDavid Ping, Head of GenAI and ML Solution Architecture for global industries at AWS, provides expert insights and practical examples to help you become a proficient ML solutions architect, linking technical architecture to business-related skills. You'll learn about ML algorithms, cloud infrastructure, system design, MLOps , and how to apply ML to solve real-world business problems. David explains the generative AI project lifecycle and examines Retrieval Augmented Generation (RAG), an effective architecture pattern for generative AI applications. You’ll also learn about open-source technologies, such as Kubernetes/Kubeflow, for building a data science environment and ML pipelines before building an enterprise ML architecture using AWS. As well as ML risk management and the different stages of AI/ML adoption, the biggest new addition to the handbook is the deep exploration of generative AI. By the end of this book , you’ll have gained a comprehensive understanding of AI/ML across all key aspects, including business use cases, data science, real-world solution architecture, risk management, and governance. You’ll possess the skills to design and construct ML solutions that effectively cater to common use cases and follow established ML architecture patterns, enabling you to excel as a true professional in the field.What you will learn Apply ML methodologies to solve business problems across industries Design a practical enterprise ML platform architecture Gain an understanding of AI risk management frameworks and techniques Build an end-to-end data management architecture using AWS Train large-scale ML models and optimize model inference latency Create a business application using artificial intelligence services and custom models Dive into generative AI with use cases, architecture patterns, and RAG Who this book is for This book is for solutions architects working on ML projects, ML engineers transitioning to ML solution architect roles, and MLOps engineers. Additionally, data scientists and analysts who want to enhance their practical knowledge of ML systems engineering, as well as AI/ML product managers and risk officers who want to gain an understanding of ML solutions and AI risk management, will also find this book useful. A basic knowledge of Python, AWS, linear algebra, probability, and cloud infrastructure is required before you get started with this handbook. |
| causal language modeling vs masked language modeling: Transformers for Machine Learning Uday Kamath, Kenneth Graham, Wael Emara, 2022-05-24 Transformers are becoming a core part of many neural network architectures, employed in a wide range of applications such as NLP, Speech Recognition, Time Series, and Computer Vision. Transformers have gone through many adaptations and alterations, resulting in newer techniques and methods. Transformers for Machine Learning: A Deep Dive is the first comprehensive book on transformers. Key Features: A comprehensive reference book for detailed explanations for every algorithm and techniques related to the transformers. 60+ transformer architectures covered in a comprehensive manner. A book for understanding how to apply the transformer techniques in speech, text, time series, and computer vision. Practical tips and tricks for each architecture and how to use it in the real world. Hands-on case studies and code snippets for theory and practical real-world analysis using the tools and libraries, all ready to run in Google Colab. The theoretical explanations of the state-of-the-art transformer architectures will appeal to postgraduate students and researchers (academic and industry) as it will provide a single entry point with deep discussions of a quickly moving field. The practical hands-on case studies and code will appeal to undergraduate students, practitioners, and professionals as it allows for quick experimentation and lowers the barrier to entry into the field. |
| causal language modeling vs masked language modeling: Beyond Quantity Andreas Sudmann, Anna Echterhölter, Markus Ramsauer, Fabian Retkowski, Jens Schröter, Alexander Waibel, 2023-11-30 How do artificial neural networks and other forms of artificial intelligence interfere with methods and practices in the sciences? Which interdisciplinary epistemological challenges arise when we think about the use of AI beyond its dependency on big data? Not only the natural sciences, but also the social sciences and the humanities seem to be increasingly affected by current approaches of subsymbolic AI, which master problems of quality (fuzziness, uncertainty) in a hitherto unknown way. But what are the conditions, implications, and effects of these (potential) epistemic transformations and how must research on AI be configured to address them adequately? |
| causal language modeling vs masked language modeling: Transfer Learning for Natural Language Processing Paul Azunre, 2021-08-31 Transfer Learning for Natural Language Processing teaches you to create powerful NLP solutions quickly by building on existing pretrained models. This instantly useful book provides crystal-clear explanations of the concepts you need to grok transfer learning along with hands-on examples so you can practice your new skills immediately. As you go, you'll apply state-of-the-art transfer learning methods to create a spam email classifier, a fact checker, and more real-world applications. |
| causal language modeling vs masked language modeling: Causal Inference and Discovery in Python Aleksander Molak, 2023-05-31 Demystify causal inference and casual discovery by uncovering causal principles and merging them with powerful machine learning algorithms for observational and experimental data Purchase of the print or Kindle book includes a free PDF eBook Key Features Examine Pearlian causal concepts such as structural causal models, interventions, counterfactuals, and more Discover modern causal inference techniques for average and heterogenous treatment effect estimation Explore and leverage traditional and modern causal discovery methods Book DescriptionCausal methods present unique challenges compared to traditional machine learning and statistics. Learning causality can be challenging, but it offers distinct advantages that elude a purely statistical mindset. Causal Inference and Discovery in Python helps you unlock the potential of causality. You’ll start with basic motivations behind causal thinking and a comprehensive introduction to Pearlian causal concepts, such as structural causal models, interventions, counterfactuals, and more. Each concept is accompanied by a theoretical explanation and a set of practical exercises with Python code. Next, you’ll dive into the world of causal effect estimation, consistently progressing towards modern machine learning methods. Step-by-step, you’ll discover Python causal ecosystem and harness the power of cutting-edge algorithms. You’ll further explore the mechanics of how “causes leave traces” and compare the main families of causal discovery algorithms. The final chapter gives you a broad outlook into the future of causal AI where we examine challenges and opportunities and provide you with a comprehensive list of resources to learn more. By the end of this book, you will be able to build your own models for causal inference and discovery using statistical and machine learning techniques as well as perform basic project assessment.What you will learn Master the fundamental concepts of causal inference Decipher the mysteries of structural causal models Unleash the power of the 4-step causal inference process in Python Explore advanced uplift modeling techniques Unlock the secrets of modern causal discovery using Python Use causal inference for social impact and community benefit Who this book is for This book is for machine learning engineers, researchers, and data scientists looking to extend their toolkit and explore causal machine learning. It will also help people who’ve worked with causality using other programming languages and now want to switch to Python, those who worked with traditional causal inference and want to learn about causal machine learning, and tech-savvy entrepreneurs who want to go beyond the limitations of traditional ML. You are expected to have basic knowledge of Python and Python scientific libraries along with knowledge of basic probability and statistics. |
| causal language modeling vs masked language modeling: Building and Fine Tuning LLMs from Scratch StoryBuddiesPlay, 2024-09-10 Building and Fine-Tuning LLMs from Scratch is an essential guide for AI practitioners, researchers, and enthusiasts looking to master the art of creating and optimizing large language models. This comprehensive resource covers everything from fundamental concepts to cutting-edge techniques, providing readers with the knowledge and skills needed to develop state-of-the-art language AI systems. With practical examples, in-depth explanations, and expert insights, this book is your roadmap to becoming proficient in LLM architecture, training, fine-tuning, and deployment. Whether you're a seasoned professional or an ambitious newcomer, this guide will empower you to push the boundaries of what's possible in natural language processing and AI. Large Language Models, AI development, Natural Language Processing, Machine Learning, Deep Learning, Transformer Architecture, Fine-tuning techniques, Neural Networks, Text Generation, Language AI |
| causal language modeling vs masked language modeling: Mastering Transformers Savaş Yıldırım, Meysam Asgari- Chenaghlu, 2021-09-15 Take a problem-solving approach to learning all about transformers and get up and running in no time by implementing methodologies that will build the future of NLP Key Features Explore quick prototyping with up-to-date Python libraries to create effective solutions to industrial problems Solve advanced NLP problems such as named-entity recognition, information extraction, language generation, and conversational AI Monitor your model's performance with the help of BertViz, exBERT, and TensorBoard Book DescriptionTransformer-based language models have dominated natural language processing (NLP) studies and have now become a new paradigm. With this book, you'll learn how to build various transformer-based NLP applications using the Python Transformers library. The book gives you an introduction to Transformers by showing you how to write your first hello-world program. You'll then learn how a tokenizer works and how to train your own tokenizer. As you advance, you'll explore the architecture of autoencoding models, such as BERT, and autoregressive models, such as GPT. You'll see how to train and fine-tune models for a variety of natural language understanding (NLU) and natural language generation (NLG) problems, including text classification, token classification, and text representation. This book also helps you to learn efficient models for challenging problems, such as long-context NLP tasks with limited computational capacity. You'll also work with multilingual and cross-lingual problems, optimize models by monitoring their performance, and discover how to deconstruct these models for interpretability and explainability. Finally, you'll be able to deploy your transformer models in a production environment. By the end of this NLP book, you'll have learned how to use Transformers to solve advanced NLP problems using advanced models.What you will learn Explore state-of-the-art NLP solutions with the Transformers library Train a language model in any language with any transformer architecture Fine-tune a pre-trained language model to perform several downstream tasks Select the right framework for the training, evaluation, and production of an end-to-end solution Get hands-on experience in using TensorBoard and Weights & Biases Visualize the internal representation of transformer models for interpretability Who this book is for This book is for deep learning researchers, hands-on NLP practitioners, as well as ML/NLP educators and students who want to start their journey with Transformers. Beginner-level machine learning knowledge and a good command of Python will help you get the best out of this book. |
Exploration of Masked and Causal Language Modelling f…
In contrast, Masked Language Modelling (MLM), primarily used for language understanding tasks, can generate …
Causal Language Modeling Vs Masked Language Modeling …
Causal Language Modeling Vs Masked Language Modeling: Large Language Models Uday Kamath,Kevin …
GPT or BERT: why not both? - ACL Anthology
Language models have become fundamental tools in natural language processing, with two dominant …
Causal and Masked Language Modeling of Javanese Langu…
V. CONCLUSION We pre-trained and compared different Transformer-based Javanese language models on two …
Cross-lingual Language Model Pretraining - NeurIPS
We investigate two unsupervised training objectives that require only monolingual corpora: Causal Language Modeling …
What Language Model Architecture and Pretraining …
We found that a causal decoder-only model pretrained with full language modeling with additional masked …
AntLM: Bridging Causal and Masked Language Models
Causal Language Modeling (CLM) and Masked Language Modeling (MLM) are two mainstream learning paradigms based …
Exploration of Masked and Causal Language Modelling for …
In contrast, Masked Language Modelling (MLM), primarily used for language understanding tasks, can generate tokens anywhere in the text and in any order. This paper conducts an extensive …
CHAPTER 11 Masked Language Models - Stanford University
Pretrained language models based on bidirectional encoders can be learned using a masked language model objective where a model is trained to guess the missing information from an …
Causal Language Modeling Vs Masked Language Modeling …
Causal Language Modeling Vs Masked Language Modeling: Large Language Models Uday Kamath,Kevin Keenan,Garrett Somers,Sarah Sorenson,2024 Large Language Models LLMs …
GPT or BERT: why not both? - ACL Anthology
Language models have become fundamental tools in natural language processing, with two dominant paradigms: causal language models (CLM) and masked language models (MLM). …
Causal and Masked Language Modeling of Javanese …
V. CONCLUSION We pre-trained and compared different Transformer-based Javanese language models on two different tasks of causal and masked language modeling. In the process, we …
Cross-lingual Language Model Pretraining - NeurIPS
We investigate two unsupervised training objectives that require only monolingual corpora: Causal Language Modeling (CLM) and Masked Language Modeling (MLM). We show that both the …
What Language Model Architecture and Pretraining …
We found that a causal decoder-only model pretrained with full language modeling with additional masked language model train-ing as a non-causal decoder-only model yields significant …
AntLM: Bridging Causal and Masked Language Models
Causal Language Modeling (CLM) and Masked Language Modeling (MLM) are two mainstream learning paradigms based on Trans-former networks, specifically the Decoder-only and …
CHAPTER 11 Fine-Tuning and Masked Lan- guage Models
ask are causal or left-to-right transformer models. In this chapter we’ll introduce a second paradigm for pretrained language mod-els, called the bidirectional transformer encoder, …
Causal Language Modeling Vs Masked Language Modeling …
Causal Language Modeling Vs Masked Language Modeling: Large Language Models Uday Kamath,Kevin Keenan,Garrett Somers,Sarah Sorenson,2024 Large Language Models LLMs …
A Meta-Learning Perspective on Transformers for Causal …
Causal language mod- eling (CLM) aims to predict the next element in a sequence in an autoregressive manner and is one of the most important applications of the Trans- former model.
Deriving Language Models from Masked Language Models
Masked language modeling has proven to be an effective paradigm for representation learning (De-vlin et al., 2019; Liu et al., 2019; He et al., 2021). However, unlike regular language …
Tutorial Proposal: Causality for Large Language Models
In this tutorial, we will explore the intersection of causality and large language models (LLMs). Our goal is to provide a comprehensive understanding of how causal inference can enhance the …
DMLM: Descriptive Masked Language Modeling - ACL …
In this work, we presented an extension of MLM called Descriptive Masked Language Modeling (DMLM), which embeds semantic information via natural language descriptions in the pre …
FCM: FORGETFUL CAUSAL MASKING MAKES CAUSAL …
inferior finetuning performance compared to masked language modeling. To address the above challenges, prior work have proposed to combine masked modeling with causal language …
CHAPTER 11 Fine-Tuning and Masked Lan- guage Models
ask are causal or left-to-right transformer models. In this chapter we’ll introduce a second paradigm for pretrained language mod-els, called the bidirectional transformer encoder, …
Causal Language Modeling Vs Masked Language Modeling .pdf
Applying Natural Language Models and Causal Models to Project Management Systems Caroline Taylor Morganti,2018 This thesis concerns itself with two problems First it examines ways in …
On the Inductive Bias of Masked Language Modeling: From …
We study how masking and predicting tokens in an unsupervised fashion can give rise to linguistic structures and downstream perfor-mance gains. Recent theories have suggested that …
Investigating Masking-based Data Generation in Language …
Recent studies have utilized masked language model to generate artificially aug-mented data for NLP downstream tasks. The experimental results show that Mask based data augmentation …
