Explainable large language models (6 Ergebnisse)

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Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book constitutes the refereed proceedings of the Second International Workshop on Advances in explainability, agents, and large language models, CALM 2025, held in Luxembourg City, Luxembourg, during December 1-5, 2025.The 8 full papers included in this book were carefully reviewed and selected from 15 submissions.The Workshop on Causality, Agents, and Large Models (CALM) fosters interdisciplinary collaboration at the intersection of causal reasoning, multi-agent systems (MAS), and large language models (LLMs). …

Taschenbuch. Zustand: Neu. Advances in Explainable Agentic AI and Large Language Models. | Second International Workshop, CALM 2025, Luxembourg City, Luxembourg, December 1-5, 2025, Proceedings | Yazan Mualla (u. a.) | Taschenbuch | Communications in Computer and Information Science | ix | Englisch | 2026 | Springer | EAN 9783032205476 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.…

Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This is a comprehensive book that explores how explainable artificial intelligence (XAI), particularly large language models (LLMs), is transforming healthcare. The book covers foundational concepts of XAI, emphasizing the need for transparency, accountability, and interpretability in AI-driven medical systems, that are crucial for clinician and patient trust. It examines the principles and methodologies in explainable AI. It details how LLMs can make complex machine learning outputs understandable through explanations, model design, and human-centered description.Part of the book is dedicated to real-world applications, such as disease diagnosis, treatment planning, and patient management. It demonstrates how XAI improves clinical decision-making and patient outcomes. It discusses the integration of explainable LLMs into electronic health records (EHRs) and clinical workflows. It shows how these technologies facilitate data analysis, improve documentation, and support care. The book also addresses the challenges and limitations of deploying explainable LLMs in healthcare. It includes issues of privacy, data complexity, and adapting models to specific domains. Evaluation techniques for explainability are discussed, with attention to metrics, benchmarks, and human-centered assessment methods that ensure AI explanations are both accurate and clinically relevant. Ethical considerations, such as fairness, accountability, and privacy, are discussed. We highlight the importance of balancing transparency with patient confidentiality. The book provides case studies and empirical evidence illustrating the benefits and challenges of implementing XAI in real clinical settings.…