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| autoformalization with large language models: Large Language Models in Cybersecurity Andrei Kucharavy, 2024 This open access book provides cybersecurity practitioners with the knowledge needed to understand the risks of the increased availability of powerful large language models (LLMs) and how they can be mitigated. It attempts to outrun the malicious attackers by anticipating what they could do. It also alerts LLM developers to understand their work's risks for cybersecurity and provides them with tools to mitigate those risks. The book starts in Part I with a general introduction to LLMs and their main application areas. Part II collects a description of the most salient threats LLMs represent in cybersecurity, be they as tools for cybercriminals or as novel attack surfaces if integrated into existing software. Part III focuses on attempting to forecast the exposure and the development of technologies and science underpinning LLMs, as well as macro levers available to regulators to further cybersecurity in the age of LLMs. Eventually, in Part IV, mitigation techniques that should allowsafe and secure development and deployment of LLMs are presented. The book concludes with two final chapters in Part V, one speculating what a secure design and integration of LLMs from first principles would look like and the other presenting a summary of the duality of LLMs in cyber-security. This book represents the second in a series published by the Technology Monitoring (TM) team of the Cyber-Defence Campus. The first book entitled Trends in Data Protection and Encryption Technologies appeared in 2023. This book series provides technology and trend anticipation for government, industry, and academic decision-makers as well as technical experts. |
| autoformalization with large language models: Bridging the Gap Between AI and Reality Bernhard Steffen, 2023-12-13 This book constitutes the proceedings of the First International Conference on Bridging the Gap between AI and Reality, AISoLA 2023, which took place in Crete, Greece, in October 2023. The papers included in this book focus on the following topics: The nature of AI-based systems; ethical, economic and legal implications of AI-systems in practice; ways to make controlled use of AI via the various kinds of formal methods-based validation techniques; dedicated applications scenarios which may allow certain levels of assistance; and education in times of deep learning. |
| autoformalization with large language models: Distributed, Ambient and Pervasive Interactions Norbert A. Streitz, |
| autoformalization with large language models: Generative AI in Teaching and Learning Hai-Jew, Shalin, 2023-12-05 Generative AI in Teaching and Learning delves into the revolutionary field of generative artificial intelligence and its impact on education. This comprehensive guide explores the multifaceted applications of generative AI in both formal and informal learning environments, shedding light on the ethical considerations and immense opportunities that arise from its implementation. From the early approaches of utilizing generative AI in teaching to its integration into various facets of learning, this book offers a profound analysis of its potential. Teachers, researchers, instructional designers, developers, data analysts, programmers, and learners alike will find valuable insights into harnessing the power of generative AI for educational purposes. |
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| autoformalization with large language models: Frontiers of Combining Systems Uli Sattler, Martin Suda, 2023-10-16 This book constitutes the refereed proceedings of the 14th International Symposium on Frontiers of Combining Systems, FroCoS 2023, held in Prague, Czech Republic, in September 2023. The symposium was co-located with the 32nd International Conference on Automated Reasoning with Analytic Tableaux and Related Methods, TABLEAUX 2023. The 14 papers presented were thorouhgly reviewed and selected from the 22 high-quality paper submissions. They are grouped in the volume according to the following topic classification: analysis of programs and equations; unification; decidable fragments; frameworks; higher-order theorem proving. This is an open access book. |
| autoformalization with large language models: Handbook of the History and Philosophy of Mathematical Practice Bharath Sriraman, |
| autoformalization with large language models: Theoretical Aspects of Software Engineering Wei-Ngan Chin, |
| autoformalization with large language models: AI, IoT, Big Data and Cloud Computing for Industry 4.0 Amy Neustein, Parikshit N. Mahalle, Prachi Joshi, Gitanjali Rahul Shinde, 2023-07-31 This book presents some of the most advanced leading-edge technology for the fourth Industrial Revolution -- known as “Industry 4.0.” The book provides a comprehensive understanding of the interconnections of AI, IoT, big data and cloud computing as integral to the technologies that revolutionize the way companies produce and distribute products and the way local governments deliver their services. The book emphasizes that at every phase of the supply chain, manufactures are found to be interweaving AI, robotics, IoT, big data/machine learning, and cloud computing into their production facilities and throughout their distribution networks. Equally important, the authors show how their research can be applied to computer vision, cyber security, database and compiler theory, natural language processing, healthcare, education and agriculture. Presents the fundamentals of AI, IoT, and cloud computing and how they can be incorporated in Industry 4.0 applications Motivates readers to address challenges in the areas of speech communication and signal processing Provides numerous examples, case studies, technical descriptions, and approaches of AI/ML |
| autoformalization with large language models: Automated Deduction – CADE 29 Brigitte Pientka, Cesare Tinelli, 2023-10-04 This open access book constitutes the proceedings of the 29th International Conference on Automated Deduction, CADE 29, which took place in Rome, Italy, during July 2023. The 28 full papers and 5 short papers presented were carefully reviewed and selected from 77 submissions. CADE is the major forum for the presentation of research in all aspects of automated deduction, including foundations, applications, implementations, and practical experience. The papers are organized in the following topics: Logical foundations; theory and principles; implementation and application; ATP and AI; and system descriptions. |
| autoformalization with large language models: PROCEEDINGS OF THE 24TH CONFERENCE ON FORMAL METHODS IN COMPUTER-AIDED DESIGN – FMCAD 2024 Nina Narodytska, Philipp Rümmer, 2024-10-01 Die Proceedings zur Konferenz „Formal Methods in Computer-Aided Design 2024“ geben aktuelle Einblicke in ein spannendes Forschungsfeld. Zum fünften Mal erscheinen die Beiträge der Konferenzreihe „Formal Methods in Computer-Aided Design“ (FMCAD) als Konferenzband bei TU Wien Academic Press. Der aktuelle Band der seit 2006 jährlich veranstalteten Konferenzreihe präsentiert in 35 Beiträgen neueste wissenschaftliche Erkenntnisse aus dem Bereich des computergestützten Entwerfens. Die Beiträge behandeln formale Aspekte des computergestützten Systemdesigns einschließlich Verifikation, Spezifikation, Synthese und Test. Die FMCAD-Konferenz findet im Oktober 2024 in Prag, Tschechische Republik, statt. Sie gilt als führendes Forum im Bereich des computer-aided design und bietet seit ihrer Gründung Forschenden sowohl aus dem akademischen als auch dem industriellen Umfeld die Möglichkeit, sich auszutauschen und zu vernetzen. |
| autoformalization with large language models: Intelligent Computer Mathematics Catherine Dubois, Manfred Kerber, 2023-08-30 This book constitutes the refereed proceedings of the 16th International Conference on Intelligent Computer Mathematics, CICM 2023, held in Cambridge, UK, in September 2023. The 14 full papers, 2 project/survey papers, 6 short papers, and 1 tool paper presented were carefully reviewed and selected from a total of 37 submissions. The papers focus on advances in formalization, automatic theorem proving and learning, search and classification, teaching and geometric reasoning, and logic and systems, among other topics. |
| autoformalization with large language models: Computer Aided Verification Constantin Enea, Akash Lal, 2023-07-17 The open access proceedings set LNCS 13964, 13965, 13966 constitutes the refereed proceedings of the 35th International Conference on Computer Aided Verification, CAV 2023, which was held in Paris, France, in July 2023. The 67 full papers presented in these proceedings were carefully reviewed and selected from 261 submissions. The have been organized in topical sections as follows: Part I: Automata and logic; concurrency; cyber-physical and hybrid systems; synthesis; Part II: Decision procedures; model checking; neural networks and machine learning; Part II: Probabilistic systems; security and quantum systems; software verification. |
| autoformalization with large language models: Intelligent Computer Mathematics Christoph Benzmüller, Bruce Miller, 2020-07-17 This book constitutes the refereed proceedings of the 13th International Conference on Intelligent Computer Mathematics, CICM 2020, held in Bertinoro, Italy, in July 2020*. The 15 full papers, 1 invited paper and 2 abstracts of invited papers presented were carefully reviewed and selected from a total of 35 submissions. The papers focus on advances in automated theorem provers and formalization, computer algebra systems and their libraries, and applications of machine learning, among other topics. * The conference was held virtually due to the COVID-19 pandemic. |
| autoformalization with large language models: Model Optimization Methods for Efficient and Edge AI Pethuru Raj Chelliah, Amir Masoud Rahmani, Robert Colby, Gayathri Nagasubramanian, Sunku Ranganath, 2024-11-13 Comprehensive overview of the fledgling domain of federated learning (FL), explaining emerging FL methods, architectural approaches, enabling frameworks, and applications Model Optimization Methods for Efficient and Edge AI explores AI model engineering, evaluation, refinement, optimization, and deployment across multiple cloud environments (public, private, edge, and hybrid). It presents key applications of the AI paradigm, including computer vision (CV) and Natural Language Processing (NLP), explaining the nitty-gritty of federated learning (FL) and how the FL method is helping to fulfill AI model optimization needs. The book also describes tools that vendors have created, including FL frameworks and platforms such as PySyft, Tensor Flow Federated (TFF), FATE (Federated AI Technology Enabler), Tensor/IO, and more. The first part of the text covers popular AI and ML methods, platforms, and applications, describing leading AI frameworks and libraries in order to clearly articulate how these tools can help with visualizing and implementing highly flexible AI models quickly. The second part focuses on federated learning, discussing its basic concepts, applications, platforms, and its potential in edge systems (such as IoT). Other topics covered include: Building AI models that are destined to solve several problems, with a focus on widely articulated classification, regression, association, clustering, and other prediction problems Generating actionable insights through a variety of AI algorithms, platforms, parallel processing, and other enablers Compressing AI models so that computational, memory, storage, and network requirements can be substantially reduced Addressing crucial issues such as data confidentiality, data access rights, data protection, and access to heterogeneous data Overcoming cyberattacks on mission-critical software systems by leveraging federated learning Written in an accessible manner and containing a helpful mix of both theoretical concepts and practical applications, Model Optimization Methods for Efficient and Edge AI is an essential reference on the subject for graduate and postgraduate students, researchers, IT professionals, and business leaders. |
| autoformalization with large language models: Automated Deduction - CADE 28 André Platzer, 2021 This open access book constitutes the proceeding of the 28th International Conference on Automated Deduction, CADE 28, held virtually in July 2021. The 29 full papers and 7 system descriptions presented together with 2 invited papers were carefully reviewed and selected from 76 submissions. CADE is the major forum for the presentation of research in all aspects of automated deduction, including foundations, applications, implementations, and practical experience. The papers are organized in the following topics: Logical foundations; theory and principles; implementation and application; ATP and AI; and system descriptions. |
| autoformalization with large language models: Tools and Algorithms for the Construction and Analysis of Systems Bernd Finkbeiner, |
| autoformalization with large language models: Logics and Type Systems in Theory and Practice Venanzio Capretta, |
| autoformalization with large language models: Intelligent Computer Mathematics Christoph Benzmüller, Bruce Miller, 2020-07-18 This book constitutes the refereed proceedings of the 13th International Conference on Intelligent Computer Mathematics, CICM 2020, held in Bertinoro, Italy, in July 2020*. The 15 full papers, 1 invited paper and 2 abstracts of invited papers presented were carefully reviewed and selected from a total of 35 submissions. The papers focus on advances in automated theorem provers and formalization, computer algebra systems and their libraries, and applications of machine learning, among other topics. * The conference was held virtually due to the COVID-19 pandemic. |
| autoformalization with large language models: Interactive Theorem Proving and Program Development Yves Bertot, Pierre Castéran, 2013-03-14 A practical introduction to the development of proofs and certified programs using Coq. An invaluable tool for researchers, students, and engineers interested in formal methods and the development of zero-fault software. |
| autoformalization with large language models: Formal Analysis of Future Energy Systems Using Interactive Theorem Proving Asad Ahmed, Osman Hasan, Falah Awwad, Nabil Bastaki, 2021-08-13 This book describes an accurate analysis technique for energy systems based on formal methods—computer-based mathematical logic techniques for the specification, validation, and verification of the systems. Correctness and accuracy of the financial, operational, and implementation analysis are of the paramount importance for the materialization of the future energy systems, such as smart grids, to achieve the objectives of cost-effectiveness, efficiency, and quality-of-service. In this regard, the book develops formal theories of microeconomics, asymptotic, and stability to support the formal analysis of generation and distribution cost, smart operations, and processing of energy in a smart grid. These formal theories are also employed to formally verify the cost and utility modeling for: Energy generation and distribution; Asymptotic bounds for online scheduling algorithms for plug-in electric vehicles; and Stability of the power converters for wind turbines. The proposed approach results in mechanized proofs for the specification, validation, and verification of corresponding smart grid problems. The formal mathematical theories developed can be applied to the formal analysis of several other hardware and software systems as well, making this book of interest to researchers and practicing engineers in a variety of power electronic fields. |
| autoformalization with large language models: Intelligent Computer Mathematics Florian Rabe, William M. Farmer, Grant O. Passmore, Abdou Youssef, 2018-08-02 This book constitutes the refereed proceedings of the 11th International Conference on Intelligent Computer Mathematics, CICM 2018, held in Hagenberg, Austria, in August 2018. The 23 full papers presented were carefully reviewed and selected from a total of 36 submissions. The papers focos on the Calculemus, Digital Mathematics Libraries, and Mathematical Knowledge Management tracks which also correspond to the subject areas of the predecessor meetings. Orthogonally, the Systems and Projects track called for descriptions of digital resources, such as data and systems, and of projects, whether old, current, or new, and survey papers covering any topics of relevance to the CICM community. |
| autoformalization with large language models: Dense Sphere Packings Thomas Callister Hales, 2012-09-06 The definitive account of the recent computer solution of the oldest problem in discrete geometry. |
| autoformalization with large language models: Artificial Intelligence with Python Alberto Artasanchez, Prateek Joshi, 2020-01-31 New edition of the bestselling guide to artificial intelligence with Python, updated to Python 3.x, with seven new chapters that cover RNNs, AI and Big Data, fundamental use cases, chatbots, and more. Key FeaturesCompletely updated and revised to Python 3.xNew chapters for AI on the cloud, recurrent neural networks, deep learning models, and feature selection and engineeringLearn more about deep learning algorithms, machine learning data pipelines, and chatbotsBook Description Artificial Intelligence with Python, Second Edition is an updated and expanded version of the bestselling guide to artificial intelligence using the latest version of Python 3.x. Not only does it provide you an introduction to artificial intelligence, this new edition goes further by giving you the tools you need to explore the amazing world of intelligent apps and create your own applications. This edition also includes seven new chapters on more advanced concepts of Artificial Intelligence, including fundamental use cases of AI; machine learning data pipelines; feature selection and feature engineering; AI on the cloud; the basics of chatbots; RNNs and DL models; and AI and Big Data. Finally, this new edition explores various real-world scenarios and teaches you how to apply relevant AI algorithms to a wide swath of problems, starting with the most basic AI concepts and progressively building from there to solve more difficult challenges so that by the end, you will have gained a solid understanding of, and when best to use, these many artificial intelligence techniques. What you will learnUnderstand what artificial intelligence, machine learning, and data science areExplore the most common artificial intelligence use casesLearn how to build a machine learning pipelineAssimilate the basics of feature selection and feature engineeringIdentify the differences between supervised and unsupervised learningDiscover the most recent advances and tools offered for AI development in the cloudDevelop automatic speech recognition systems and chatbotsApply AI algorithms to time series dataWho this book is for The intended audience for this book is Python developers who want to build real-world Artificial Intelligence applications. Basic Python programming experience and awareness of machine learning concepts and techniques is mandatory. |
| autoformalization with large language models: Isabelle Lawrence C. Paulson, 1994-07-28 This volume presents the proceedings of the First International Static Analysis Symposium (SAS '94), held in Namur, Belgium in September 1994. The proceedings comprise 25 full refereed papers selected from 70 submissions as well as four invited contributions by Charles Consel, Saumya K. Debray, Thomas W. Getzinger, and Nicolas Halbwachs. The papers address static analysis aspects for various programming paradigms and cover the following topics: generic algorithms for fixpoint computations; program optimization, transformation and verification; strictness-related analyses; type-based analyses and type inference; dependency analyses and abstract domain construction. |
| autoformalization with large language models: Intelligent Computer Mathematics Cezary Kaliszyk, Edwin Brady, Andrea Kohlhase, Claudio Sacerdoti Coen, 2019-07-02 This book constitutes the refereed proceedings of the 12th International Conference on Intelligent Computer Mathematics, CICM 2019, held in Prague, Czech Republic, in July 2019. The 19 full papers presented were carefully reviewed and selected from a total of 41 submissions. The papers focus on digital and computational solutions which are becoming the prevalent means for the generation, communication, processing, storage and curation of mathematical information. Separate communities have developed to investigate and build computer based systems for computer algebra, automated deduction, and mathematical publishing as well as novel user interfaces. While all of these systems excel in their own right, their integration can lead to synergies offering significant added value. |
| autoformalization with large language models: The Seventeen Provers of the World Freek Wiedijk, 2006-02-03 Commemorating the 50th anniversary of the first time a mathematical theorem was proven by a computer system, Freek Wiedijk initiated the present book in 2004 by inviting formalizations of a proof of the irrationality of the square root of two from scientists using various theorem proving systems. The 17 systems included in this volume are among the most relevant ones for the formalization of mathematics. The systems are showcased by presentation of the formalized proof and a description in the form of answers to a standard questionnaire. The 17 systems presented are HOL, Mizar, PVS, Coq, Otter/Ivy, Isabelle/Isar, Alfa/Agda, ACL2, PhoX, IMPS, Metamath, Theorema, Leog, Nuprl, Omega, B method, and Minlog. |
| autoformalization with large language models: Transformer Condition Control Vasily Ya. Ushakov, Alexey V. Mytnikov, Valeriy A. Lavrinovich, Alexey V. Lavrinovich, 2021-09-01 This book is devoted to one of the main problems of modern electrical power engineering—power transformer diagnostics. The first three chapters discuss the fundamentals: The first chapter presents the physical reasons for power transformers’ failures and the technical and economic consequences of disruption of the normal operation. The second chapter reviews the standard technologies for monitoring the state of the high-voltage transformers. The third chapter tells about monitoring the condition of transformer windings based on the pulse method. The fourth chapter presents the technologies for transformer windings condition controlled by means of nanosecond pulses. The stages of improving the pulsed method based on a short probing pulse of the nanosecond range, the results of experiments on identifying the radial and axial displacements of the winding, studies of the effect of the duration and shape of the probing pulse on the sensitivity of the diagnostic procedure, and the stages of developing a mathematical as well as physical model of a power transformer are consistently presented. |
| autoformalization with large language models: First-Order Logic and Automated Theorem Proving Melvin Fitting, 2012-12-06 There are many kinds of books on formal logic. Some have philosophers as their intended audience, some mathematicians, some computer scientists. Although there is a common core to all such books they will be very dif ferent in emphasis, methods, and even appearance. This book is intended for computer scientists. But even this is not precise. Within computer sci ence formal logic turns up in a number of areas, from program verification to logic programming to artificial intelligence. This book is intended for computer scientists interested in automated theorem proving in classical logic. To be more precise yet, it is essentially a theoretical treatment, not a how-to book, although how-to issues are not neglected. This does not mean, of course, that the book will be of no interest to philosophers or mathematicians. It does contain a thorough presentation of formal logic and many proof techniques, and as such it contains all the material one would expect to find in a course in formal logic covering completeness but not incompleteness issues. The first item to be addressed is, what are we talking about and why are we interested in it. We are primarily talking about truth as used in mathematical discourse, and our interest in it is, or should be, self-evident. Truth is a semantic concept, so we begin with models and their properties. These are used to define our subject. |
| autoformalization with large language models: Diagrammatic Representation and Inference Mateja Jamnik, Yuri Uesaka, Stephanie Elzer Schwartz, 2016-07-25 This book constitutes the refereed proceedings of the 9th InternationalConference on the Theory and Application of Diagrams, Diagrams 2016,held in Philadelphia, PA, USA, in August 2016. The 12 revised full papers and 11 short papers presented together with 5 posters were carefully reviewed and selected from 48 submissions. The papers are organized in the following topical sections: cognitive aspects of diagrams; logic and diagrams; Euler and Venn diagrams; diagrams and education; design principles for diagrams; diagrams layout. |
| autoformalization with large language models: Automated Deduction, Cade-12. Alan Bundy, 1994-06-08 This volume contains the reviewed papers presented at the 12th International Conference on Automated Deduction (CADE-12) held at Nancy, France in June/July 1994. The 67 papers presented were selected from 177 submissions and document many of the most important research results in automated deduction since CADE-11 was held in June 1992. The volume is organized in chapters on heuristics, resolution systems, induction, controlling resolutions, ATP problems, unification, LP applications, special-purpose provers, rewrite rule termination, ATP efficiency, AC unification, higher-order theorem proving, natural systems, problem sets, and system descriptions. |
| autoformalization with large language models: Time Warps, String Edits, and Macromolecules David Sankoff, Joseph B. Kruskal, 1983 The book is the first, and still best compilation of papers explaining how to measure distance between sequences, and how to compute that measure effectively. |
| autoformalization with large language models: Machine Learning: ECML 2006 Johannes Fürnkranz, Tobias Scheffer, Myra Spiliopoulou, 2006-09-21 This book constitutes the refereed proceedings of the 17th European Conference on Machine Learning, ECML 2006, held, jointly with PKDD 2006. The book presents 46 revised full papers and 36 revised short papers together with abstracts of 5 invited talks, carefully reviewed and selected from 564 papers submitted. The papers present a wealth of new results in the area and address all current issues in machine learning. |
| autoformalization with large language models: Automated Reasoning Alessandro Armando, Peter Baumgartner, Gilles Dowek, 2008-07-25 methods, description logics and related logics, sati?ability modulo theory, decidable logics, reasoning about programs, and higher-order logics. |
| autoformalization with large language models: Models and Computability S. Barry Cooper, John K. Truss, Association for Symbolic Logic, 1999-06-17 Second of two volumes providing a comprehensive guide to the current state of mathematical logic. |
| autoformalization with large language models: Automated Reasoning Didier Galmiche, Stephan Schulz, Roberto Sebastiani, 2018-07-01 This book constitutes the refereed proceedings of the 9th International Joint Conference on Automated Reasoning, IJCAR 2018, held in Oxford, United Kingdom, in July 2018, as part of the Federated Logic Conference, FLoC 2018. In 2018, IJCAR unites CADE, TABLEAUX, and FroCoS, the International Symposium on Frontiers of Combining Systems, and, for the fourth time, is part of the Federated Logic Conference. The 38 revised full research papers and 8 system descriptions presented together with two invited talks were carefully reviewed and selected from 108 submissions. The papers focus on topics such as logics, deductive systems, proof-search methods, theorem proving, model checking, verification, formal methods, and program analysis. |
| autoformalization with large language models: The Weil Conjectures Karen Olsson, 2019-07-16 A New York Times Editors' Pick and Paris Review Staff Pick A wonderful book. --Patti Smith I was riveted. Olsson is evocative on curiosity as an appetite of the mind, on the pleasure of glutting oneself on knowledge. --Parul Sehgal, The New York Times An eloquent blend of memoir and biography exploring the Weil siblings, math, and creative inspiration Karen Olsson’s stirring and unusual third book, The Weil Conjectures, tells the story of the brilliant Weil siblings—Simone, a philosopher, mystic, and social activist, and André, an influential mathematician—while also recalling the years Olsson spent studying math. As she delves into the lives of these two singular French thinkers, she grapples with their intellectual obsessions and rekindles one of her own. For Olsson, as a math major in college and a writer now, it’s the odd detours that lead to discovery, to moments of insight. Thus The Weil Conjectures—an elegant blend of biography and memoir and a meditation on the creative life. Personal, revealing, and approachable, The Weil Conjectures eloquently explores math as it relates to intellectual history, and shows how sometimes the most inexplicable pursuits turn out to be the most rewarding. |
| autoformalization with large language models: Introduction to HOL Michael J. C. Gordon, Tom F. Melham, 1993 Higher-Order Logic (HOL) is a proof development system intended for applications to both hardware and software. It is principally used in two ways: for directly proving theorems, and as theorem-proving support for application-specific verification systems. HOL is currently being applied to a wide variety of problems, including the specification and verification of critical systems. Introduction to HOL provides a coherent and self-contained description of HOL containing both a tutorial introduction and most of the material that is needed for day-to-day work with the system. After a quick overview that gives a hands-on feel for the way HOL is used, there follows a detailed description of the ML language. The logic that HOL supports and how this logic is embedded in ML, are then described in detail. This is followed by an explanation of the theorem-proving infrastructure provided by HOL. Finally two appendices contain a subset of the reference manual, and an overview of the HOL library, including an example of an actual library documentation. |
| autoformalization with large language models: Intelligent Computer Mathematics Fairouz Kamareddine, Claudio Sacerdoti Coen, 2021 This book constitutes the refereed proceedings of the 14th International Conference on Intelligent Computer Mathematics, CICM 2021, held in Timisoara, Romania, in July 2021*. The 12 full papers, 7 system descriptions, 1 system entry, and 3 abstracts of invited papers presented were carefully reviewed and selected from a total of 38 submissions. The papers focus on advances in formalization, automatic theorem proving and learning, search and classification, teaching and geometric reasoning, and logic and systems, among other topics. * The conference was held virtually due to the COVID-19 pandemic. |
| autoformalization with large language models: Diagrammatic Representation and Reasoning Michael Anderson, Bernd Meyer, Patrick Olivier, 2011-06-27 The rise in computing and multimedia technology has spawned an increasing interest in the role of diagrams and sketches, not only for the purpose of conveying information but also for creative thinking and problem-solving. This book attempts to characterise the nature of a science of diagrams in a wide-ranging, multidisciplinary study that contains accounts of the most recent research results in computer science and psychology. Key topics include: cognitive aspects, formal aspects, and applications. It is a well-written and indispensable survey for researchers and students in the fields of cognitive science, artificial intelligence, human-computer interaction, and graphics and visualisation. |
A New Approach Towards Autoformalization - arXiv.org
Autoformalization is the task of automatically translating natural language mathematics into a formal language that can be verified by a program. This is a challenging task, and especially …
Autoformalizing Mathematical Statements by Symbolic …
of autoformalization remains limited because traditional methods often necessitate either predefined domain-specific languages or hard-coded translation rules [15–18]. Recently, large …
Assessing and Understanding Creativity in Large Language …
Assessing and Understanding Creativity in Large Language Models Yunpu Zhao1,Rui Zhang2 Wenyi Li3,5, Di Huang2, Jiaming Guo2,Shaohui Peng3 Yifan Hao2, Yuanbo Wen2, Xing …
Autoformalization With Large Language Models (2024)
Autoformalization With Large Language Models eBook Subscription Services Autoformalization With Large Language Models Budget-Friendly Options 6. Navigating Autoformalization With …
Autoformalizing Euclidean Geometry - arXiv.org
a language for writing formal proofs. It is popular among mathematicians and has a growing ecosystem of integration with large language models (LLMs), e.g., LeanDojo (Yang et al.,2023) …
Position: Trustworthy AI Agents Require the Integration of …
Large Language Models and Formal Methods ... LLM for Autoformalization Autoformalization is the process of automatically translating natural language-based specifications or informal …
FVEL: Interactive Formal Verification Environment with Large …
Jun 21, 2024 · FVEL: Interactive Formal Verification Environment with Large Language Models via Theorem Proving Xiaohan Lin 1 ∗Qingxing Cao Yinya Huang2 Haiming Wang3 Jianqiao Lu4 …
Autoformalization With Large Language Models (book)
Autoformalization With Large Language Models Large Language Models in Cybersecurity Andrei Kucharavy,2024 This open access book provides cybersecurity practitioners with the …
StepProof: Step-by-step verification of natural language …
Large Language Model: In recent years, large language models (LLMs) have achieved outstanding performance in many downstream tasks of natural language processing. LLMs …
Autoformalization in the Era of Large Language Models: A …
tion from informal mathematics to formal language. As a key component of the broader field of mathematical artificial in-telligence, autoformalization seeks to bridge the gap between human …
D , S AND PROVE: GUIDING FORMAL PROVERS WITH …
of few-shot statement autoformalization. Namely, a small number of examples are enough for them to learn to perform informal-to-formal translation of statements. In this paper, we …
Autoformalization With Large Language Models [PDF]
Large Language Models in Cybersecurity Andrei Kucharavy,2024 This open access book provides cybersecurity practitioners with the knowledge needed to understand the risks of the …
Autoformalization in the Era of Large Language Models: A …
tion from informal mathematics to formal language. As a key component of the broader field of mathematical artificial in-telligence, autoformalization seeks to bridge the gap between human …
PDE-Controller: LLMs for Autoformalization and Reasoning …
and reasoning of PDE control problems using large language models. 3.1. Overview Problem Definition. As introduced in Sec.2, the input of a PDE control problem is the natural language …
Project Description: Experiments with Language Models for …
Learning-assisted autoformalization [5] may offer a promising path to this challenge. It oper-ates as a subset of machine translation tasks [8] in which (large) language models (LMs/LLMs) …
Autoformalization With Large Language Models Copy
accessing Autoformalization With Large Language Models versions, you eliminate the need to spend money on physical copies. This not only saves you money but also reduces the …
Autoformalization With Large Language Models
Dec 11, 2023 · Large Language Models in Cybersecurity Andrei Kucharavy,2024 This open access book provides cybersecurity practitioners with the knowledge needed to understand the …
Autoformalization With Large Language Models (book)
Large Language Models in Cybersecurity Andrei Kucharavy,2024 This open access book provides cybersecurity practitioners with the knowledge needed to understand the risks of the …
Autoformalization With Large Language Models
Autoformalization With Large Language Models (2023) Autoformalization With Large Language Models AI, IoT, Big Data and Cloud Computing for Industry 4.0 - Amy Neustein 2023-07-31 …
Autoformalization With Large Language Models
Oct 16, 2023 · Autoformalization With Large Language Models Catherine Dubois,Manfred Kerber Large Language Models in Cybersecurity Andrei Kucharavy,2024 This open access book …
A Study of Knowledge Distillation for Theorem Proving in …
While gaps exist even in large language models autoformaliza-tion, we hope for this to serve as an exploration of the effectiveness of knowledge distillation in autoformalization, a task that has …
What Can Large Language Models Do for Theorem Proving …
What Can Large Language Models Do for Theorem Proving and Formal Methods? Moa Johansson(B) Chalmers University of Technology, Gothenburg, Sweden …
Autoformalization With Large Language Models Copy
Adventure: Autoformalization With Large Language Models . This immersive experience, available for download in a PDF format ( *), transports you to the heart of natural marvels and thrilling …
Autoformalization With Large Language Models
LLMs: The Engine Behind Autoformalization Large language models are the key ingredient in the autoformalization recipe. Trained on massive datasets of text and code, these models develop …
Improving Autoformalization using Type Checking - arXiv.org
task referred to as autoformalization (Szegedy, 2020). Current state-of-the-art autoformalization methods rely on the few-shot formalization capabilities of large language models (Wu et al., …
Consistent Autoformalization for Constructing Mathematical …
Autoformalization is the task of automatically translating mathematical content written in nat-ural language to a formal language expression. The growing language interpretation capabil-ities of …
Generative Agents for Multi-Agent Autoformalization of …
2.2 Large Language Models The rapid advancement of natural language processing (NLP), driven by transformer architectures [12] and pre-trained models [27], has led to the emergence of …
Autoformalization With Large Language Models [PDF]
Large Language Models in Cybersecurity Andrei Kucharavy,2024 This open access book provides cybersecurity practitioners with the knowledge needed to understand the risks of the …
Autoformalization With Large Language Models …
Large Language Models in Cybersecurity Andrei Kucharavy,2024 This open access book provides cybersecurity practitioners with the knowledge needed to understand the risks of the …
Autoformalization With Large Language Models (book)
Large Language Models in Cybersecurity Andrei Kucharavy,2024 This open access book provides cybersecurity practitioners with the knowledge needed to understand the risks of the …
A arXiv:2311.03755v2 [cs.CL] 9 Nov 2023
•We train the first language model that can autoformalize to multiple languages in the zero-shot setting, and manually evaluate it on two autoformalization benchmarks. •We verify that: (1) …
A S S ROLE OF DATA QUALITY AND ALIGNMENT FOR F …
Large Language Models (LLMs) for the task of autoformalization. Contrary to the conventional emphasis on dataset size, our research highlights the importance of ... Autoformalization with …
Abstract - arXiv.org
Wu et al., 2021, Lample et al., 2022]. Since the search space is significantly large, the searching process consumes considerable time and computing resources. Another series of ATP …
Autoformalization With Large Language Models
Large Language Models in Cybersecurity Andrei Kucharavy,2024 This open access book provides cybersecurity practitioners with the knowledge needed to understand the risks of the …
Position: Trustworthy AI Agents Require the Integration of …
Large Language Models and Formal Methods Yedi Zhang * 1Yufan Cai Xinyue Zuo Xiaokun Luan* 2 Kailong Wang* 3 Zhe Hou4 Yifan Zhang1 ... Autoformalization is the process of …
Improving the Diproche CNL through Autoformalization via …
3 Prompting and Training Large Language Models The pre-trained language models offered by OpenAI can be adapted to a specific task in two different ways: Prompting and fine-tuning. …
Autoformalization With Large Language Models
Autoformalization With Large Language Models Catherine Dubois,Manfred Kerber Large Language Models in Cybersecurity Andrei Kucharavy,2024 This open access book provides …
Autoformalization With Large Language Models (book)
Large Language Models in Cybersecurity Andrei Kucharavy,2024 This open access book provides cybersecurity practitioners with the knowledge needed to understand the risks of the …
Autoformalization With Large Language Models (book)
Large Language Models in Cybersecurity Andrei Kucharavy,2024 This open access book provides cybersecurity practitioners with the knowledge needed to understand the risks of the …
Autoformalization With Large Language Models
Autoformalization With Large Language Models Bernhard Steffen Large Language Models in Cybersecurity Andrei Kucharavy,2024 This open access book provides cybersecurity …
Autoformalization With Large Language Models
Autoformalization With Large Language Models Wei-Ngan Chin Large Language Models in Cybersecurity Andrei Kucharavy,2024 This open access book provides cybersecurity …
Multi-language Diversity Benefits Autoformalization
Experiments show that language models fine-tuned onMMA can produce up to 29−31% of statements acceptable with minimal corrections on the miniF2F and ProofNet benchmarks, up …
Improving the Diproche CNL through Autoformalization via …
3 Prompting and Training Large Language Models The pre-trained language models offered by OpenAI can be adapted to a specific task in two different ways: Prompting and fine-tuning. …
Autoformalization With Large Language Models
Autoformalization With Large Language Models Wei-Ngan Chin Large Language Models in Cybersecurity Andrei Kucharavy,2024 This open access book provides cybersecurity …
Autoformalization With Large Language Models
Autoformalization With Large Language Models Wei-Ngan Chin Large Language Models in Cybersecurity Andrei Kucharavy,2024 This open access book provides cybersecurity …
A Survey on Deep Learning for Theorem Proving - arXiv.org
the emergence of large language models, has sparked a notable surge of research exploring these techniques to enhance the process of theorem ... Wang et al. (2018; 2020) first explore …
Abstract Process-Driven Autoformalization - ResearchGate
to evaluate the autoformalization capabilities of large language models (LLMs). This benchmark encompasses a comprehensive assessment of questions, answers, formal statements, and …
Autoformalization With Large Language Models
Autoformalization With Large Language Models Catherine Dubois,Manfred Kerber Large Language Models in Cybersecurity Andrei Kucharavy,2024 This open access book provides …
