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| discovering latent knowledge in language models without supervision: Language Processing and Knowledge in the Web Iryna Gurevych, Chris Biemann, Torsten Zesch, 2013-09-13 This book constitutes the refereed conference proceedings of the 25th International Conference on Language Processing and Knowledge in the Web, GSCL 2013, held in Darmstadt, Germany, in September 2013. The 20 revised full papers were carefully selected from numerous submissions and cover topics on language processing and knowledge in the Web on several important dimensions, such as computational linguistics, language technology, and processing of unstructured textual content in the Web. |
| discovering latent knowledge in language models without supervision: Algorithms of Intelligence: Exploring the World of Machine Learning Dr R. Keerthika, Ms.S.S.Abinayaa, 2022-01-20 Delve into the fascinating world of machine learning with this comprehensive guide, which unpacks the algorithms driving today's intelligent systems. From foundational concepts to advanced applications, this book is essential for anyone looking to understand the mechanics behind AI. |
| discovering latent knowledge in language models without supervision: Advances in Artificial Intelligence Malek Mouhoub, Philippe Langlais, 2017-05-06 This book constitutes the refereed proceedings of the 30th Canadian Conference on Artificial Intelligence, Canadian AI 2017, held in Edmonton, AB, Canada, in May 2017. The 19 regular papers and 24 short papers presented together with 6 Graduate Student Symposium papers were carefully reviewed and selected from 62 submissions. The focus of the conference was on the following subjects: Data Mining and Machine Learning; Planning and Combinatorial Optimization; AI Applications; Natural Language Processing; Uncertainty and Preference Reasoning; and Agent Systems. |
| discovering latent knowledge in language models without supervision: Representation Learning for Natural Language Processing Zhiyuan Liu, Yankai Lin, Maosong Sun, 2020-07-03 This open access book provides an overview of the recent advances in representation learning theory, algorithms and applications for natural language processing (NLP). It is divided into three parts. Part I presents the representation learning techniques for multiple language entries, including words, phrases, sentences and documents. Part II then introduces the representation techniques for those objects that are closely related to NLP, including entity-based world knowledge, sememe-based linguistic knowledge, networks, and cross-modal entries. Lastly, Part III provides open resource tools for representation learning techniques, and discusses the remaining challenges and future research directions. The theories and algorithms of representation learning presented can also benefit other related domains such as machine learning, social network analysis, semantic Web, information retrieval, data mining and computational biology. This book is intended for advanced undergraduate and graduate students, post-doctoral fellows, researchers, lecturers, and industrial engineers, as well as anyone interested in representation learning and natural language processing. |
| discovering latent knowledge in language models without supervision: Computer Vision -- ECCV 2014 David Fleet, Tomas Pajdla, Bernt Schiele, Tinne Tuytelaars, 2014-08-14 The seven-volume set comprising LNCS volumes 8689-8695 constitutes the refereed proceedings of the 13th European Conference on Computer Vision, ECCV 2014, held in Zurich, Switzerland, in September 2014. The 363 revised papers presented were carefully reviewed and selected from 1444 submissions. The papers are organized in topical sections on tracking and activity recognition; recognition; learning and inference; structure from motion and feature matching; computational photography and low-level vision; vision; segmentation and saliency; context and 3D scenes; motion and 3D scene analysis; and poster sessions. |
| discovering latent knowledge in language models without supervision: Lifelong Machine Learning, Second Edition Zhiyuan Sun, Bing Leno da Silva, 2022-06-01 Lifelong Machine Learning, Second Edition is an introduction to an advanced machine learning paradigm that continuously learns by accumulating past knowledge that it then uses in future learning and problem solving. In contrast, the current dominant machine learning paradigm learns in isolation: given a training dataset, it runs a machine learning algorithm on the dataset to produce a model that is then used in its intended application. It makes no attempt to retain the learned knowledge and use it in subsequent learning. Unlike this isolated system, humans learn effectively with only a few examples precisely because our learning is very knowledge-driven: the knowledge learned in the past helps us learn new things with little data or effort. Lifelong learning aims to emulate this capability, because without it, an AI system cannot be considered truly intelligent. Research in lifelong learning has developed significantly in the relatively short time since the first edition of this book was published. The purpose of this second edition is to expand the definition of lifelong learning, update the content of several chapters, and add a new chapter about continual learning in deep neural networks—which has been actively researched over the past two or three years. A few chapters have also been reorganized to make each of them more coherent for the reader. Moreover, the authors want to propose a unified framework for the research area. Currently, there are several research topics in machine learning that are closely related to lifelong learning—most notably, multi-task learning, transfer learning, and meta-learning—because they also employ the idea of knowledge sharing and transfer. This book brings all these topics under one roof and discusses their similarities and differences. Its goal is to introduce this emerging machine learning paradigm and present a comprehensive survey and review of the important research results and latest ideas in the area. This book is thus suitable for students, researchers, and practitioners who are interested in machine learning, data mining, natural language processing, or pattern recognition. Lecturers can readily use the book for courses in any of these related fields. |
| discovering latent knowledge in language models without supervision: Speech & Language Processing Dan Jurafsky, 2000-09 |
| discovering latent knowledge in language models without supervision: Pattern Recognition and Machine Learning Christopher M. Bishop, 2016-08-23 This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory. |
| discovering latent knowledge in language models without supervision: Handbook of Mixed Membership Models and Their Applications Edoardo M. Airoldi, David Blei, Elena A. Erosheva, Stephen E. Fienberg, 2014-11-06 Incorporating more than 20 years of the editors' and contributors' statistical work in mixed membership modeling, this handbook shows how to use these flexible modeling tools to uncover hidden patterns in modern high-dimensional multivariate data. It explores the use of the models in various application settings, including survey data, population genetics, text analysis, image processing and annotation, and molecular biology. Through examples using real data sets, readers will discover how to characterize complex multivariate data in a range of areas. |
| discovering latent knowledge in language models without supervision: Semi-Supervised Learning Olivier Chapelle, Bernhard Scholkopf, Alexander Zien, 2010-01-22 A comprehensive review of an area of machine learning that deals with the use of unlabeled data in classification problems: state-of-the-art algorithms, a taxonomy of the field, applications, benchmark experiments, and directions for future research. In the field of machine learning, semi-supervised learning (SSL) occupies the middle ground, between supervised learning (in which all training examples are labeled) and unsupervised learning (in which no label data are given). Interest in SSL has increased in recent years, particularly because of application domains in which unlabeled data are plentiful, such as images, text, and bioinformatics. This first comprehensive overview of SSL presents state-of-the-art algorithms, a taxonomy of the field, selected applications, benchmark experiments, and perspectives on ongoing and future research.Semi-Supervised Learning first presents the key assumptions and ideas underlying the field: smoothness, cluster or low-density separation, manifold structure, and transduction. The core of the book is the presentation of SSL methods, organized according to algorithmic strategies. After an examination of generative models, the book describes algorithms that implement the low-density separation assumption, graph-based methods, and algorithms that perform two-step learning. The book then discusses SSL applications and offers guidelines for SSL practitioners by analyzing the results of extensive benchmark experiments. Finally, the book looks at interesting directions for SSL research. The book closes with a discussion of the relationship between semi-supervised learning and transduction. |
| discovering latent knowledge in language models without supervision: Transforming the Workforce for Children Birth Through Age 8 National Research Council, Institute of Medicine, Board on Children, Youth, and Families, Committee on the Science of Children Birth to Age 8: Deepening and Broadening the Foundation for Success, 2015-07-23 Children are already learning at birth, and they develop and learn at a rapid pace in their early years. This provides a critical foundation for lifelong progress, and the adults who provide for the care and the education of young children bear a great responsibility for their health, development, and learning. Despite the fact that they share the same objective - to nurture young children and secure their future success - the various practitioners who contribute to the care and the education of children from birth through age 8 are not acknowledged as a workforce unified by the common knowledge and competencies needed to do their jobs well. Transforming the Workforce for Children Birth Through Age 8 explores the science of child development, particularly looking at implications for the professionals who work with children. This report examines the current capacities and practices of the workforce, the settings in which they work, the policies and infrastructure that set qualifications and provide professional learning, and the government agencies and other funders who support and oversee these systems. This book then makes recommendations to improve the quality of professional practice and the practice environment for care and education professionals. These detailed recommendations create a blueprint for action that builds on a unifying foundation of child development and early learning, shared knowledge and competencies for care and education professionals, and principles for effective professional learning. Young children thrive and learn best when they have secure, positive relationships with adults who are knowledgeable about how to support their development and learning and are responsive to their individual progress. Transforming the Workforce for Children Birth Through Age 8 offers guidance on system changes to improve the quality of professional practice, specific actions to improve professional learning systems and workforce development, and research to continue to build the knowledge base in ways that will directly advance and inform future actions. The recommendations of this book provide an opportunity to improve the quality of the care and the education that children receive, and ultimately improve outcomes for children. |
| discovering latent knowledge in language models without supervision: Human-Like Machine Intelligence Stephen Muggleton, Nicholas Chater, 2021 This book, authored by an array of internationally recognised researchers, is of direct relevance to all those involved in Academia and Industry wanting to obtain insights into the topics at the forefront of the revolution in Artificial Intelligence and Cognitive Science. |
| discovering latent knowledge in language models without supervision: Data Mining Krzysztof J. Cios, Witold Pedrycz, Roman W. Swiniarski, Lukasz Andrzej Kurgan, 2007-10-05 This comprehensive textbook on data mining details the unique steps of the knowledge discovery process that prescribes the sequence in which data mining projects should be performed, from problem and data understanding through data preprocessing to deployment of the results. This knowledge discovery approach is what distinguishes Data Mining from other texts in this area. The book provides a suite of exercises and includes links to instructional presentations. Furthermore, it contains appendices of relevant mathematical material. |
| discovering latent knowledge in language models without supervision: Semantic Theory Jerrold J. Katz, 1972 |
| discovering latent knowledge in language models without supervision: Multimodal Scene Understanding Michael Ying Yang, Bodo Rosenhahn, Vittorio Murino, 2019-07-16 Multimodal Scene Understanding: Algorithms, Applications and Deep Learning presents recent advances in multi-modal computing, with a focus on computer vision and photogrammetry. It provides the latest algorithms and applications that involve combining multiple sources of information and describes the role and approaches of multi-sensory data and multi-modal deep learning. The book is ideal for researchers from the fields of computer vision, remote sensing, robotics, and photogrammetry, thus helping foster interdisciplinary interaction and collaboration between these realms. Researchers collecting and analyzing multi-sensory data collections – for example, KITTI benchmark (stereo+laser) - from different platforms, such as autonomous vehicles, surveillance cameras, UAVs, planes and satellites will find this book to be very useful. - Contains state-of-the-art developments on multi-modal computing - Shines a focus on algorithms and applications - Presents novel deep learning topics on multi-sensor fusion and multi-modal deep learning |
| discovering latent knowledge in language models without supervision: On the Move to Meaningful Internet Systems: OTM 2012 Workshops Pilar Herrero, Herve Panetto, Robert Meersman, Tharam Dillon, 2013-01-17 This volume constitutes the refereed proceedings of ten international workshops, OTM Academy, Industry Case Studies Program, EI2N, INBAST, Meta4eS, OnToContent, ORM, SeDeS, SINCOM and SOMOCO 2012, held as part of OTM 2012 in Rome, Italy, in September 2012. The 66 revised full papers presented were carefully reviewed and selected from a total of 127 submissions. The volume also includes 7 papers from the On the Move Academy (OTMA) 2012 as well as 4 CoopIS 2012 poster papers and 5 ODBASE 2012 poster papers. The paper cover various aspects of computer supported cooperative work (CSCW), middleware, Internet/Web data management, electronic commerce, enterprise modelling, workflow management, knowledge flow, agent technologies, information retrieval, software architectures, service-oriented computing, and cloud computing. |
| discovering latent knowledge in language models without supervision: Reinforcement Learning, second edition Richard S. Sutton, Andrew G. Barto, 2018-11-13 The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence. Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics. Like the first edition, this second edition focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning's relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning. |
| discovering latent knowledge in language models without supervision: Practical Weak Supervision Wee Hyong Tok, Amit Bahree, Senja Filipi, 2021-09-30 Most data scientists and engineers today rely on quality labeled data to train machine learning models. But building a training set manually is time-consuming and expensive, leaving many companies with unfinished ML projects. There's a more practical approach. In this book, Wee Hyong Tok, Amit Bahree, and Senja Filipi show you how to create products using weakly supervised learning models. You'll learn how to build natural language processing and computer vision projects using weakly labeled datasets from Snorkel, a spin-off from the Stanford AI Lab. Because so many companies have pursued ML projects that never go beyond their labs, this book also provides a guide on how to ship the deep learning models you build. Get up to speed on the field of weak supervision, including ways to use it as part of the data science process Use Snorkel AI for weak supervision and data programming Get code examples for using Snorkel to label text and image datasets Use a weakly labeled dataset for text and image classification Learn practical considerations for using Snorkel with large datasets and using Spark clusters to scale labeling |
| discovering latent knowledge in language models without supervision: Applications of Topic Models Jordan Boyd-Graber, Yuening Hu, David Mimno, 2017-07-13 Describes recent academic and industrial applications of topic models with the goal of launching a young researcher capable of building their own applications of topic models. |
| discovering latent knowledge in language models without supervision: Introduction to Semi-Supervised Learning Xiaojin Geffner, Andrew Bazzan, 2022-05-31 Semi-supervised learning is a learning paradigm concerned with the study of how computers and natural systems such as humans learn in the presence of both labeled and unlabeled data. Traditionally, learning has been studied either in the unsupervised paradigm (e.g., clustering, outlier detection) where all the data are unlabeled, or in the supervised paradigm (e.g., classification, regression) where all the data are labeled. The goal of semi-supervised learning is to understand how combining labeled and unlabeled data may change the learning behavior, and design algorithms that take advantage of such a combination. Semi-supervised learning is of great interest in machine learning and data mining because it can use readily available unlabeled data to improve supervised learning tasks when the labeled data are scarce or expensive. Semi-supervised learning also shows potential as a quantitative tool to understand human category learning, where most of the input is self-evidently unlabeled. In this introductory book, we present some popular semi-supervised learning models, including self-training, mixture models, co-training and multiview learning, graph-based methods, and semi-supervised support vector machines. For each model, we discuss its basic mathematical formulation. The success of semi-supervised learning depends critically on some underlying assumptions. We emphasize the assumptions made by each model and give counterexamples when appropriate to demonstrate the limitations of the different models. In addition, we discuss semi-supervised learning for cognitive psychology. Finally, we give a computational learning theoretic perspective on semi-supervised learning, and we conclude the book with a brief discussion of open questions in the field. Table of Contents: Introduction to Statistical Machine Learning / Overview of Semi-Supervised Learning / Mixture Models and EM / Co-Training / Graph-Based Semi-Supervised Learning / Semi-Supervised Support Vector Machines / Human Semi-Supervised Learning / Theory and Outlook |
| discovering latent knowledge in language models without supervision: Distributed Language Stephen J. Cowley, 2011-10-05 The volume presents language as fully integrated with human existence. On this view, language is not essentially ‘symbolic’, not represented inside minds or brains, and most certainly not determined by micro-social rules and norms. Rather, language is part of our ecology. It emerges when bodies co-ordinate vocal and visible gesture to integrate events with different histories. Enacting feeling, expression and wordings, language permeates the collective, individual and affective life of living beings. It is a profoundly distributed, multi-centric activity that binds people together as they go about their lives. Distributed Language pursues this perspective both theoretically and in relation to empirical work. Empirically, it reports studies on the anticipatory dynamics of reading, its socio-cognitive consequences, Shakespearean theatre, what images evoke (in brain and word), and solving insight problems. Theoretically, the volume challenges linguistic autonomy from overlapping theoretical positions. First, it is argued that language exploits a species specific form of semiotic cognition. Second, it is suggested that the central function of language lies in realizing values that derive from our ecosystemic existence. Third, this is ascribed to how cultural and biological symbols co-regulate the dynamics that shape human activity. Fourth, it is argued that language, far from being organism-centred, gives us an extended ecology in which our co-ordination is saturated by values and norms that are derived from our sociocultural environment. The contributions to this volume expand on those originally published in Pragmatics & Cognition 17:3 (2009). |
| discovering latent knowledge in language models without supervision: Constrained Clustering Sugato Basu, Ian Davidson, Kiri Wagstaff, 2008-08-18 Since the initial work on constrained clustering, there have been numerous advances in methods, applications, and our understanding of the theoretical properties of constraints and constrained clustering algorithms. Bringing these developments together, Constrained Clustering: Advances in Algorithms, Theory, and Applications presents an extensive collection of the latest innovations in clustering data analysis methods that use background knowledge encoded as constraints. Algorithms The first five chapters of this volume investigate advances in the use of instance-level, pairwise constraints for partitional and hierarchical clustering. The book then explores other types of constraints for clustering, including cluster size balancing, minimum cluster size,and cluster-level relational constraints. Theory It also describes variations of the traditional clustering under constraints problem as well as approximation algorithms with helpful performance guarantees. Applications The book ends by applying clustering with constraints to relational data, privacy-preserving data publishing, and video surveillance data. It discusses an interactive visual clustering approach, a distance metric learning approach, existential constraints, and automatically generated constraints. With contributions from industrial researchers and leading academic experts who pioneered the field, this volume delivers thorough coverage of the capabilities and limitations of constrained clustering methods as well as introduces new types of constraints and clustering algorithms. |
| discovering latent knowledge in language models without supervision: Resources in Education , 1981 |
| discovering latent knowledge in language models without supervision: 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.” |
| discovering latent knowledge in language models without supervision: Disease Control Priorities, Third Edition (Volume 6) King K. Holmes, Stefano Bertozzi, Barry R. Bloom, Prabhat Jha, 2017-11-06 Infectious diseases are the leading cause of death globally, particularly among children and young adults. The spread of new pathogens and the threat of antimicrobial resistance pose particular challenges in combating these diseases. Major Infectious Diseases identifies feasible, cost-effective packages of interventions and strategies across delivery platforms to prevent and treat HIV/AIDS, other sexually transmitted infections, tuberculosis, malaria, adult febrile illness, viral hepatitis, and neglected tropical diseases. The volume emphasizes the need to effectively address emerging antimicrobial resistance, strengthen health systems, and increase access to care. The attainable goals are to reduce incidence, develop innovative approaches, and optimize existing tools in resource-constrained settings. |
| discovering latent knowledge in language models without supervision: Leveraging Data Science for Global Health Leo Anthony Celi, Maimuna S. Majumder, Patricia Ordóñez, Juan Sebastian Osorio, Kenneth E. Paik, Melek Somai, 2020-07-31 This open access book explores ways to leverage information technology and machine learning to combat disease and promote health, especially in resource-constrained settings. It focuses on digital disease surveillance through the application of machine learning to non-traditional data sources. Developing countries are uniquely prone to large-scale emerging infectious disease outbreaks due to disruption of ecosystems, civil unrest, and poor healthcare infrastructure – and without comprehensive surveillance, delays in outbreak identification, resource deployment, and case management can be catastrophic. In combination with context-informed analytics, students will learn how non-traditional digital disease data sources – including news media, social media, Google Trends, and Google Street View – can fill critical knowledge gaps and help inform on-the-ground decision-making when formal surveillance systems are insufficient. |
| discovering latent knowledge in language models without supervision: Text as Data Justin Grimmer, Margaret E. Roberts, Brandon M. Stewart, 2022-03-29 A guide for using computational text analysis to learn about the social world From social media posts and text messages to digital government documents and archives, researchers are bombarded with a deluge of text reflecting the social world. This textual data gives unprecedented insights into fundamental questions in the social sciences, humanities, and industry. Meanwhile new machine learning tools are rapidly transforming the way science and business are conducted. Text as Data shows how to combine new sources of data, machine learning tools, and social science research design to develop and evaluate new insights. Text as Data is organized around the core tasks in research projects using text—representation, discovery, measurement, prediction, and causal inference. The authors offer a sequential, iterative, and inductive approach to research design. Each research task is presented complete with real-world applications, example methods, and a distinct style of task-focused research. Bridging many divides—computer science and social science, the qualitative and the quantitative, and industry and academia—Text as Data is an ideal resource for anyone wanting to analyze large collections of text in an era when data is abundant and computation is cheap, but the enduring challenges of social science remain. Overview of how to use text as data Research design for a world of data deluge Examples from across the social sciences and industry |
| discovering latent knowledge in language models without supervision: Foundations of Rational Agency Michael Wooldridge, A. Rao, 2013-03-09 This volume represents an advanced, comprehensive state-of-the-art survey of the field of rational agency as it stands today. It covers the philosophical foundations of rational agency, logical and decision-theoretic approaches to rational agency, multi-agent aspects of rational agency and a number of approaches to programming rational agents. It will be of interest to researchers in logic, mainstream computer science, the philosophy of rational action and agency, and economics. |
| discovering latent knowledge in language models without supervision: The Archaeology of Knowledge Michel Foucault, 2012-07-11 Madness, sexuality, power, knowledge—are these facts of life or simply parts of speech? In a series of works of astonishing brilliance, historian Michel Foucault excavated the hidden assumptions that govern the way we live and the way we think. The Archaeology of Knowledge begins at the level of things aid and moves quickly to illuminate the connections between knowledge, language, and action in a style at once profound and personal. A summing up of Foucault's own methadological assumptions, this book is also a first step toward a genealogy of the way we live now. Challenging, at times infuriating, it is an absolutey indispensable guide to one of the most innovative thinkers of our time. |
| discovering latent knowledge in language models without supervision: Explainable AI: Interpreting, Explaining and Visualizing Deep Learning Wojciech Samek, Grégoire Montavon, Andrea Vedaldi, Lars Kai Hansen, Klaus-Robert Müller, 2019-09-10 The development of “intelligent” systems that can take decisions and perform autonomously might lead to faster and more consistent decisions. A limiting factor for a broader adoption of AI technology is the inherent risks that come with giving up human control and oversight to “intelligent” machines. For sensitive tasks involving critical infrastructures and affecting human well-being or health, it is crucial to limit the possibility of improper, non-robust and unsafe decisions and actions. Before deploying an AI system, we see a strong need to validate its behavior, and thus establish guarantees that it will continue to perform as expected when deployed in a real-world environment. In pursuit of that objective, ways for humans to verify the agreement between the AI decision structure and their own ground-truth knowledge have been explored. Explainable AI (XAI) has developed as a subfield of AI, focused on exposing complex AI models to humans in a systematic and interpretable manner. The 22 chapters included in this book provide a timely snapshot of algorithms, theory, and applications of interpretable and explainable AI and AI techniques that have been proposed recently reflecting the current discourse in this field and providing directions of future development. The book is organized in six parts: towards AI transparency; methods for interpreting AI systems; explaining the decisions of AI systems; evaluating interpretability and explanations; applications of explainable AI; and software for explainable AI. |
| discovering latent knowledge in language models without supervision: Graph Representation Learning William L. William L. Hamilton, 2022-06-01 Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D vision, recommender systems, question answering, and social network analysis. This book provides a synthesis and overview of graph representation learning. It begins with a discussion of the goals of graph representation learning as well as key methodological foundations in graph theory and network analysis. Following this, the book introduces and reviews methods for learning node embeddings, including random-walk-based methods and applications to knowledge graphs. It then provides a technical synthesis and introduction to the highly successful graph neural network (GNN) formalism, which has become a dominant and fast-growing paradigm for deep learning with graph data. The book concludes with a synthesis of recent advancements in deep generative models for graphs—a nascent but quickly growing subset of graph representation learning. |
| discovering latent knowledge in language models without supervision: Bandit Algorithms Tor Lattimore, Csaba Szepesvári, 2020-07-16 A comprehensive and rigorous introduction for graduate students and researchers, with applications in sequential decision-making problems. |
| discovering latent knowledge in language models without supervision: Knowledge Graphs Aidan Hogan, Eva Blomqvist, Michael Cochez, Claudia d’Amato, Gerard de Melo, Claudio Gutierrez, Sabrina Kirrane, Jose Emilio Labra Gayo, Roberto Navigli, Sebastian Neumaier, Axel-Cyrille Ngonga Ngomo, Axel Polleres, Sabbir M. Rashid, Anisa Rula, Juan Sequeda, Lukas Schmelzeisen, Steffen Staab, Antoine Zimmermann, 2021-11-08 This book provides a comprehensive and accessible introduction to knowledge graphs, which have recently garnered notable attention from both industry and academia. Knowledge graphs are founded on the principle of applying a graph-based abstraction to data, and are now broadly deployed in scenarios that require integrating and extracting value from multiple, diverse sources of data at large scale. The book defines knowledge graphs and provides a high-level overview of how they are used. It presents and contrasts popular graph models that are commonly used to represent data as graphs, and the languages by which they can be queried before describing how the resulting data graph can be enhanced with notions of schema, identity, and context. The book discusses how ontologies and rules can be used to encode knowledge as well as how inductive techniques—based on statistics, graph analytics, machine learning, etc.—can be used to encode and extract knowledge. It covers techniques for the creation, enrichment, assessment, and refinement of knowledge graphs and surveys recent open and enterprise knowledge graphs and the industries or applications within which they have been most widely adopted. The book closes by discussing the current limitations and future directions along which knowledge graphs are likely to evolve. This book is aimed at students, researchers, and practitioners who wish to learn more about knowledge graphs and how they facilitate extracting value from diverse data at large scale. To make the book accessible for newcomers, running examples and graphical notation are used throughout. Formal definitions and extensive references are also provided for those who opt to delve more deeply into specific topics. |
| discovering latent knowledge in language models without supervision: Machine Learning Maria Johnsen, 2024-07-06 Machine learning has revolutionized industries, from healthcare to entertainment, by enhancing how we understand and interact with data. Despite its prevalence, mastering this field requires both theoretical knowledge and practical skills. This book bridges that gap, starting with foundational concepts and essential mathematics, then advancing through a wide range of algorithms and techniques. It covers supervised and unsupervised learning, neural networks, deep learning, and reinforcement learning, with clear explanations and practical examples. Real-world applications are highlighted through scenarios and case studies, demonstrating how to solve specific problems with machine learning. You'll find hands-on guides to popular tools and libraries like Python, Scikit-Learn, TensorFlow, Keras, and PyTorch, enabling you to build, evaluate, and deploy models effectively. The book explores cutting-edge topics like quantum machine learning and explainable AI, keeping you updated on the latest trends. Detailed case studies and capstone projects provide practical experience, guiding you through the entire machine learning process. This book, a labor of love born from extensive research and passion, aims to make machine learning accessible and engaging. Machine learning is about curiosity, creativity, and the pursuit of knowledge. Explore, experiment, and enjoy the journey. Thank you for choosing this book. I am excited to be part of your machine learning adventure and look forward to the incredible things you will achieve. |
| discovering latent knowledge in language models without supervision: Bulletin of the Atomic Scientists , 1970-12 The Bulletin of the Atomic Scientists is the premier public resource on scientific and technological developments that impact global security. Founded by Manhattan Project Scientists, the Bulletin's iconic Doomsday Clock stimulates solutions for a safer world. |
| discovering latent knowledge in language models without supervision: Text Analytics with Python Dipanjan Sarkar, 2016-11-30 Derive useful insights from your data using Python. You will learn both basic and advanced concepts, including text and language syntax, structure, and semantics. You will focus on algorithms and techniques, such as text classification, clustering, topic modeling, and text summarization. Text Analytics with Python teaches you the techniques related to natural language processing and text analytics, and you will gain the skills to know which technique is best suited to solve a particular problem. You will look at each technique and algorithm with both a bird's eye view to understand how it can be used as well as with a microscopic view to understand the mathematical concepts and to implement them to solve your own problems. What You Will Learn: Understand the major concepts and techniques of natural language processing (NLP) and text analytics, including syntax and structure Build a text classification system to categorize news articles, analyze app or game reviews using topic modeling and text summarization, and cluster popular movie synopses and analyze the sentiment of movie reviews Implement Python and popular open source libraries in NLP and text analytics, such as the natural language toolkit (nltk), gensim, scikit-learn, spaCy and Pattern Who This Book Is For : IT professionals, analysts, developers, linguistic experts, data scientists, and anyone with a keen interest in linguistics, analytics, and generating insights from textual data |
| discovering latent knowledge in language models without supervision: Person Re-Identification Shaogang Gong, Marco Cristani, Shuicheng Yan, Chen Change Loy, 2014-01-03 The first book of its kind dedicated to the challenge of person re-identification, this text provides an in-depth, multidisciplinary discussion of recent developments and state-of-the-art methods. Features: introduces examples of robust feature representations, reviews salient feature weighting and selection mechanisms and examines the benefits of semantic attributes; describes how to segregate meaningful body parts from background clutter; examines the use of 3D depth images and contextual constraints derived from the visual appearance of a group; reviews approaches to feature transfer function and distance metric learning and discusses potential solutions to issues of data scalability and identity inference; investigates the limitations of existing benchmark datasets, presents strategies for camera topology inference and describes techniques for improving post-rank search efficiency; explores the design rationale and implementation considerations of building a practical re-identification system. |
| discovering latent knowledge in language models without supervision: Using Mplus for Structural Equation Modeling E. Kevin Kelloway, 2014-07-22 Ideal for researchers and graduate students in the social sciences who require knowledge of structural equation modeling techniques to answer substantive research questions, Using Mplus for Structural Equation Modeling provides a reader-friendly introduction to the major types of structural equation models implemented in the Mplus framework. This practical book, which updates author E. Kevin Kelloway’s 1998 book Using LISREL for Structural Equation Modeling, retains the successful five-step process employed in the earlier book, with a thorough update for use in the Mplus environment. Kelloway provides an overview of structural equation modeling techniques in Mplus, including the estimation of confirmatory factor analysis and observed variable path analysis. He also covers multilevel modeling for hypothesis testing in real life settings and offers an introduction to the extended capabilities of Mplus, such as exploratory structural equation modeling and estimation and testing of mediated relationships. A sample application with the source code, printout, and results is presented for each type of analysis. ”An excellent book on the ins and outs of using Mplus, as well as the practice of structural equation modeling in applied research.” —Kevin J. Grimm, University of California, Davis |
| discovering latent knowledge in language models without supervision: 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. |
| discovering latent knowledge in language models without supervision: 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. |
DISCOVERING LATENT KNOWLEDGE IN LANGUAGE MODELS …
We propose circumventing this issue by directly finding latent knowledge inside the internal activations of a language model in a purely unsupervised way. Specifically, we introduce a …
B Confident Bro: Discovering Latent Knowledge In Language …
CCS, introduced by [1], is a method that tries to address eliciting latent knowledge from a [language] model (about what the model knows) in an unsupervised fashion, without requiring …
Processing London, AY 24-25 — Natural Language …
soning in Large Language Models: A Survey. [pdf] Collin B, Haotian Y, Dan K, Jacob S. 2022. Discovering Latent Knowledge In Language. Models Without Supervision. [pdf] Almog G, Elad …
Latent Factor Models Meets Instructions: Goal-conditioned …
Instruct-LF generates a set of natural language property descriptions from data, i.e., documents (1a); then estimates the compatibility between each data point and each property (1b), and …
AI Testing Hackathon Write up - Discovering Latent …
Our work seeks to improve upon the paper “Discovering Latent Knowledge in Language Models without supervision”. This paper introduces a novel method to determine a language model’s …
arXiv:2212.03827v2 [cs.CL] 2 Mar 2024
despite using no supervision and no model outputs, our method can recover diverse knowledge represented in large language models: across 6 models and 10 question-answering datasets, it …
Eliciting Latent Knowledge from Language Reward Models
Our method proposes how to integrate these probes with a pre-trained language model to build reward models for “honesty”. We demonstrate that using these newly built reward models to …
Discovering Latent Knowledge In Language Models Without …
Discovering Latent Knowledge In Language Models Without Supervision books and manuals for download have transformed the way we access information. They provide a cost-effective and …
Leveraging large language models for knowledge-free weak …
We propose an approach leveraging fine‐tuning LLMs and weak supervision with virtually no domain knowledge that still achieves consistently dominant performance. Using a …
Challenges with unsupervised LLM knowledge discovery
We reveal novel pathologies in existing unsupervised methods seeking to discover latent knowledge from large language model (LLM) activations—instead of knowl-edge they seem to …
Discovering Latent Knowledge In Language Models Without …
Discovering Latent Knowledge In Language Models Without Supervision: Learning Better Latent Representations from Semantic Knowledge Pengxiang Cheng,2020 Many modern efforts in …
Language Models are Unsupervised Multitask Learners - OpenAI
We demonstrate that language models begin to learn these tasks without any ex-plicit supervision when trained on a new dataset of millions of webpages called WebText.
arXiv:2212.03827v1 [cs.CL] 7 Dec 2022
despite using no supervision and no model outputs, our method can recover diverse knowledge represented in large language models: across 6 models and 10 question-answering datasets, it …
Surely You're Lying, Mr. Model: Improving and Analyzing CCS
Contrast Consistent Search (Burns et al., 2022) is a method for eliciting latent knowledge without supervision. In this paper, we explore a few di-rections for improving CCS. We use conjunctive …
Eliciting Latent Knowledge from “Quirky” Language Models
Our results show promise for eliciting reliable knowl-edge from capable but untrusted models, and facilitates future research empirically investigating ELK methods. Large language models show …
Latent Lab: Large Language Models for Knowledge …
This paper investigates the potential of AI models, par-ticularly large language models (LLMs), to support knowledge exploration and augment human creativity during ideation. We present …
Self-Specialization: Uncovering Latent Expertise within Large …
In our preliminary study, we find that existing models such as Alpaca (Taori et al., 2023) and Dromedary (Sun et al., 2023), although aligned, exhibit only a modest degree of improve-ment …
Discovering Latent Knowledge In Language Models Without …
Discovering Latent Knowledge In Language Models Without Supervision: Language Processing and Knowledge in the Web Iryna Gurevych,Chris Biemann,Torsten Zesch,2013-09-13 This …
arXiv:2106.09231v1 [cs.CL] 17 Jun 2021
The great success of Pre-trained Language Models (PLMs) raises the question of whether PLMs can be directly used as reliable knowledge bases.Petroni et al.(2019) propose the LAMA …
Self-Specialization: Uncovering Latent Expertise within Large …
Recent works have demonstrated the effective- ness of self-alignment in which a large lan- guage model is aligned to follow general in- structions using instructional data generated from the …
Eliciting Latent Knowledge from “Quirky” Language Models
Eliciting Latent Knowledge from “Quirky” Language Models Alex Mallen1∗, Madeline Brumley 2, Julia Kharchenko2, Nora Belrose1 1EleutherAI 2University of Washington Abstract Eliciting …
Discovering Bias in Latent Space: An Unsupervised Debiasing …
Large Language Models (LLMs) and Vision-Language Mod-els (VLMs) show impressive performance on benchmark question-answering tasks, even in some cases outperform-ing …
Open-world Multi-label Text Classication with Extremely Weak …
language descriptor. However, these methods still require access to the ground-truth label space. Open-world Single-label Text Classication: There has been a surge in open-world models …
SELF-SPECIALIZATION: UNCOVERING LATENT EX PERTISE …
tise in various domains is mixed and latent within base LLMs. Target domain expertise is carved out through self-specialization. Instruction-tuning (Ouyang et al., 2022; Wei et al., 2022; Mishra …
Knowledge-Retrieval Task-Oriented Dialog Systems with …
system to improve the quality of knowledge selection. 2.2. Knowledge Retriever for Conditional Generation Recent researches such as RAG [ 10 ] and REALM [ 20 ] have introduced …
Eliciting Latent Knowledge from “Quirky” Language Models
Eliciting Latent Knowledge from “Quirky” Language Models Alex Mallen1∗, Madeline Brumley 2, Julia Kharchenko2, Nora Belrose1 1EleutherAI 2University of Washington Abstract Eliciting …
Discovering Bias in Latent Space: An Unsupervised
Large Language Models (LLMs) and Vision-Language Mod-els (VLMs) show impressive performance on benchmark question-answering tasks, even in some cases outperform-ing …
Inference-Time Intervention: Eliciting Truthful Answers from a …
quality knowledge graphs from LLMs without human supervision. Kadavath et al. (2022) find language models can generate and then self-evaluate their own answers with high accuracy. …
NoiseCLR: A Contrastive Learning Approach for Unsupervised …
demand extensive domain knowledge to create appropriate text prompts. This limitation highlights the significance of discovering directions in the latent space in an unsupervised manner. A …
Inference-Time Intervention: Eliciting Truthful Answers from a …
quality knowledge graphs from LLMs without human supervision. Kadavath et al. (2022) find language models can generate and then self-evaluate their own answers with high accuracy. …
Does It Know?: Probing and Benchmarking Uncertainty in …
occurring after) the knowledge limitations of the model. TYMES serves as a valuable proof-of-concept for how we can benchmark uncertainty or time-sensitive world knowledge in language …
Learning a natural-language to LTL executable semantic …
Keywords: LTL, semantic parsing, weak supervision 1 Introduction Natural language has the potential to be the most effective and convenient way to issue commands to ... Where this …
Inference-Time Intervention: Eliciting Truthful Answers from a …
quality knowledge graphs from LLMs without human supervision. Kadavath et al. (2022) find language models can generate and then self-evaluate their own answers with high accuracy. …
Activation Addition: Steering Language Models Without …
Language Model of Dathathri et al. 2020. This uses a sepa-rate classifier (one classifier per attribute to steer towards) to perturb the model’s activations to generate text that accords more …
Inference-Time Intervention: Eliciting Truthful Answers from a …
Jun 27, 2024 · quality knowledge graphs from LLMs without human supervision. Kadavath et al. (2022) find language models can generate and then self-evaluate their own answers with high …
Discovering Bias in Latent Space: An Unsupervised Debiasing …
Large Language Models (LLMs) and Vision-Language Mod-els (VLMs) show impressive performance on benchmark question-answering tasks, even in some cases outperform-ing …
Monitoring Latent World States in Language Models with
Monitoring Latent World States in Language Models with Propositional Probes Jiahai Feng∗ Stuart Russell Jacob Steinhardt UC Berkeley Abstract Language models are susceptible to …
Inference-Time Intervention: Eliciting Truthful Answers from a …
quality knowledge graphs from LLMs without human supervision. Kadavath et al. (2022) find language models can generate and then self-evaluate their own answers with high accuracy. …
Cluster-Norm for Unsupervised Probing of Knowledge
between knowledge in general and simulated knowledge—a major issue in the literature of latent knowledge elicitation (Christiano et al., 2021)—it significantly improves the ability of …
Discovering Dialogue Slots with Weak Supervision - ACL …
aims at explicit dialogue state modeling without the need for any in-domain supervision. 3 Method Our slot discovery method has three main stages: (1) We obtain weak supervision labels from …
ARE P LANGUAGE MODELS AWARE OF P ? SIMPLE BUT …
With the recent success and popularity of pre-trained language models (LMs) in natural language processing, there has been a rise in efforts to understand their in-ner workings. In line with …
Offline Multi-Agent Reinforcement Learning with Knowledge …
environment and extract knowledge from each other to enhance their learning. M&M [7] combines curriculum learning as well as distillation to allow agents to perform well in an environment with …
Eliciting Latent Knowledge from “Quirky” Language Models
Eliciting Latent Knowledge from “Quirky” Language Models Alex Mallen1∗, Madeline Brumley 2, Julia Kharchenko2, Nora Belrose1 1EleutherAI 2University of Washington Abstract Eliciting …
Unelicitable Backdoors in Language Models via …
Recent advances in language modelling have led to the wide proliferation of fine-tuned models, available for download on the internet. As of this writing, the web repository HuggingFace [Wolf …
Topic Discovery via Latent Space Clustering of Pretrained …
Topic Discovery, Pretrained Language Models, Clustering ACM Reference Format: Yu Meng, Yunyi Zhang, Jiaxin Huang, Yu Zhang, Jiawei Han. 2022. Topic Discovery via Latent Space …
Activation Addition: Steering Language Models Without …
Language Model of Dathathri et al. 2020. This uses a sepa-rate classifier (one classifier per attribute to steer towards) to perturb the model’s activations to generate text that accords more …
DISCOVERING L CONCEPTS LEARNED IN BERT - arXiv.org
to pre-defined concepts that reinforce the traditional linguistic knowledge and do not reflect on how novel concepts are learned by the model. We address this lim-itation by discovering and …
Conditional Prototype Rectification Prompt Learning - arXiv.org
the trained visual-language models (VLMs) embody critical general knowledge, thereby exhibiting enhanced generaliz-ability across diverse tasks. In recent developments, several visual …
Latent Relation Language Models - arXiv.org
3 Latent Relation Language Models Next we describe our proposed framework of La-tent Relation Language Models (LRLMs). 3.1 Motivation The goal of the conditional language model-ing task …
Abstract arXiv:1905.11975v4 [cs.CL] 7 Aug 2020
Representations for Text without Supervision Peng Xu 1Jackie Chi Kit Cheung1 2 3 Yanshuai Cao Abstract The variational autoencoder (VAE) can learn the manifold of natural images on certain …
Abstract - arXiv.org
MMNeuron: Discovering Neuron-Level Domain-Specific Interpretation in Multimodal Large Language Model Jiahao Huo 1,3, Yibo Yan 2, Boren Hu , Yutao Yue , Xuming Hu1,2* 1 The …
Self-Specialization: Uncovering Latent Expertise within Large …
structions, exploiting the latent domain knowledge within large pre-trained models. Domain-Specific Instruction Generation. Leveraging the seed demonstrations, we prompt a base …
Skill Induction and Planning with Latent Language - ACL …
models to translate those instructions into actions. But applications of language-based supervision for long-horizon policy learning have remained quite limited in scope. Current LLP training …
arXiv:2310.08887v2 [cs.LG] 10 Mar 2024
The goal of unsupervised RL is to acquire useful knowledge, such as policies, world models, or exploratory data, by interacting with the environment in an unsupervised manner (i.e., without …
Natural Language Processing - University of California, San …
Contents Contents 1 Preface i Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i How to use this book ...
3URPSWLQJ &&53URELQJ - arXiv.org
ing knowledge and in-context ranking capacities without supervision? Knowing the answer to this question would allow us to uncover knowledge gaps, outdated information and existing biases …
Towards Reliable Latent Knowledge Estimation in LLMs:Zero …
parison of latent knowledge of open source LLMs at scale: In contrast to prompt-based LKEs [20, 35], which are tailored to specific relations and models, ZP-LKE creates a single input to test …
arXiv:2502.15147v2 [cs.CL] 27 Apr 2025
Latent Factor Models Meets Instructions: Goal-conditioned Latent Factor Discovery without Task Supervision Zhouhang Xie1 Tushar Khot 2Bhavana Dalvi Mishra Harshit Surana ... Algorithms …
Leveraging Natural Supervision for Language …
Recent breakthroughs in Natural Language Processing (NLP) have been driven by language models trained on a massive amount of plain text. While powerful, deriv-ing supervision from …
BEH Benchmarking Honesty in Large Language Models
the precise knowledge boundaries for a model is a significant challenge. Thus, for scenario 2, we approximate the knowledge boundary of a model through multiple temperature sampling. …
Abstract arXiv:1905.11975v4 [cs.CL] 7 Aug 2020
Representations for Text without Supervision Peng Xu 1Jackie Chi Kit Cheung1 2 3 Yanshuai Cao Abstract The variational autoencoder (VAE) can learn the manifold of natural images on certain …
Discovering the Latent Writing Style from Articles: A
Computational Linguistics and Chinese Language Processing Vol. 24, No. 1, June 2019, pp. 15-38 15 The Association for Computational Linguistics and Chinese Language Processing …
Self-Specialization: Uncovering Latent Expertise within Large …
outperform their base models by a large mar-gin, and even larger models that are generally instruction-tuned or that have been adapted to the target domain by other means. 1 …
Hierarchical Topic Mining via Joint Spherical Tree and …
Due to their e˛ectiveness of discovering organized topic structures automatically without human supervision, hierarchical topic models have been applied to a wide range of applications …
Discover and Mitigate Multiple Biased Subgroups in Image …
ful supervision from the image classifier. We further inter-pret the semantic meaning of each subgroup component by generating natural language descriptions using vision-language …
DISCOVERING LATENT CONCEPTS LEARNED IN BERT
Published as a conference paper at ICLR 2022 DISCOVERING LATENT CONCEPTS LEARNED IN BERT Fahim Dalvi⋄∗Abdul Rafae Khan†∗ Firoj Alam ⋄Nadir Durrani⋄ Jia Xu† Hassan Sajjad …
Latent Factor Models Meets Instructions: Goal-conditioned …
Latent Factor Models Meets Instructions: Goal-conditioned Latent Factor Discovery without Task Supervision Zhouhang Xie 1 Tushar Khot 2 Bhavana Dalvi Mishra 2 Harshit Surana 2 Julian …
Self-Specialization: Uncovering Latent Expertise within Large …
Mar 5, 2024 · Self-Specialization: Uncovering Latent Expertise within Large Language Models Anonymous ACL submission Abstract 001 Recent works have demonstrated the effective- 002 …
PoLLMgraph: Unraveling Hallucinations in Large Language …
(e.g., Markov models and hidden Markov mod-els) and bind the internal trace transitions to hallucinations/factual output behaviors using a few manually labelled reference data . Upon …
Latent Relation Language Models
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