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Introduction to foundation models

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Introduction to foundation models/ by Pin-Yu Chen, Sijia Liu.
Author:	Chen, Pin-Yu.
other author:	Liu, Sijia.
Published:	Cham :Springer Nature Switzerland : : 2025.,
Description:	xiii, 310 p. :ill., digital ; : 24 cm.;
Contained By:	Springer Nature eBook
Subject:	Machine learning. -
Online resource:	https://doi.org/10.1007/978-3-031-76770-8
ISBN:	9783031767708

Introduction to foundation models
Chen, Pin-Yu.

Introduction to foundation models[electronic resource] /by Pin-Yu Chen, Sijia Liu. - Cham :Springer Nature Switzerland :2025. - xiii, 310 p. :ill., digital ;24 cm.

Part I-Fundamentals of Foundation Models -- Chapter 1-Foundation Models and Generative AI -- Chapter 2-Neural Networks -- Chapter 3- Learning and Generalization of Vision Transformers -- Chapter 4-Formalizing In-Context Learning in Transformers -- Part II Advanced Topics in Foundation Model -- Chapter 5-Automated Visual Prompting -- Chapter 6-Prompting Large Language Models with Privacy -- Chapter 7- Memory-Efficient Fine-Tuning for Foundation Models -- Chapter 8 Large Language Models Meet Time Series -- Chapter 9-Large Language Models Meet Speech Recognition -- Chapter 10-Benchmarking Foundation Models using Synthetic Datasets -- Chapter 11-Machine Unlearning for Foundation Models -- Chapter 12-Part III Trust and Safety in Foundation Models -- Chapter 12-Trustworthiness Evaluation of Large Language Models -- Chapter 13-Attacks and Defenses on Aligned Large Language Models -- Chapter 14- Safety Risks in Fine-tuning Large Language Models -- Chapter15- Watermarks for Large Language Models -- Chapter 16- AI-Generated Text Detection -- Chapter 17- Backdoor Risks in Diffusion Models -- Chapter 18- Prompt Engineering for Safety Red-teaming: A Case Study on Text-to-Image Diffusion Models.

This book offers an extensive exploration of foundation models, guiding readers through the essential concepts and advanced topics that define this rapidly evolving research area. Designed for those seeking to deepen their understanding and contribute to the development of safer and more trustworthy AI technologies, the book is divided into three parts providing the fundamentals, advanced topics in foundation modes, and safety and trust in foundation models: Part I introduces the core principles of foundation models and generative AI, presents the technical background of neural networks, delves into the learning and generalization of transformers, and finishes with the intricacies of transformers and in-context learning. Part II introduces automated visual prompting techniques, prompting LLMs with privacy, memory-efficient fine-tuning methods, and shows how LLMs can be reprogrammed for time-series machine learning tasks. It explores how LLMs can be reused for speech tasks, how synthetic datasets can be used to benchmark foundation models, and elucidates machine unlearning for foundation models. Part III provides a comprehensive evaluation of the trustworthiness of LLMs, introduces jailbreak attacks and defenses for LLMs, presents safety risks when find-tuning LLMs, introduces watermarking techniques for LLMs, presents robust detection of AI-generated text, elucidates backdoor risks in diffusion models, and presents red-teaming methods for diffusion models. Mathematical notations are clearly defined and explained throughout, making this book an invaluable resource for both newcomers and seasoned researchers in the field.

ISBN: 9783031767708

Standard No.: 10.1007/978-3-031-76770-8doiSubjects--Topical Terms:

561253
Machine learning.

LC Class. No.: Q325.5 / .C44 2025

Dewey Class. No.: 006.31

Introduction to foundation models
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505 0 $a Part I-Fundamentals of Foundation Models -- Chapter 1-Foundation Models and Generative AI -- Chapter 2-Neural Networks -- Chapter 3- Learning and Generalization of Vision Transformers -- Chapter 4-Formalizing In-Context Learning in Transformers -- Part II Advanced Topics in Foundation Model -- Chapter 5-Automated Visual Prompting -- Chapter 6-Prompting Large Language Models with Privacy -- Chapter 7- Memory-Efficient Fine-Tuning for Foundation Models -- Chapter 8 Large Language Models Meet Time Series -- Chapter 9-Large Language Models Meet Speech Recognition -- Chapter 10-Benchmarking Foundation Models using Synthetic Datasets -- Chapter 11-Machine Unlearning for Foundation Models -- Chapter 12-Part III Trust and Safety in Foundation Models -- Chapter 12-Trustworthiness Evaluation of Large Language Models -- Chapter 13-Attacks and Defenses on Aligned Large Language Models -- Chapter 14- Safety Risks in Fine-tuning Large Language Models -- Chapter15- Watermarks for Large Language Models -- Chapter 16- AI-Generated Text Detection -- Chapter 17- Backdoor Risks in Diffusion Models -- Chapter 18- Prompt Engineering for Safety Red-teaming: A Case Study on Text-to-Image Diffusion Models.
520 $a This book offers an extensive exploration of foundation models, guiding readers through the essential concepts and advanced topics that define this rapidly evolving research area. Designed for those seeking to deepen their understanding and contribute to the development of safer and more trustworthy AI technologies, the book is divided into three parts providing the fundamentals, advanced topics in foundation modes, and safety and trust in foundation models: Part I introduces the core principles of foundation models and generative AI, presents the technical background of neural networks, delves into the learning and generalization of transformers, and finishes with the intricacies of transformers and in-context learning. Part II introduces automated visual prompting techniques, prompting LLMs with privacy, memory-efficient fine-tuning methods, and shows how LLMs can be reprogrammed for time-series machine learning tasks. It explores how LLMs can be reused for speech tasks, how synthetic datasets can be used to benchmark foundation models, and elucidates machine unlearning for foundation models. Part III provides a comprehensive evaluation of the trustworthiness of LLMs, introduces jailbreak attacks and defenses for LLMs, presents safety risks when find-tuning LLMs, introduces watermarking techniques for LLMs, presents robust detection of AI-generated text, elucidates backdoor risks in diffusion models, and presents red-teaming methods for diffusion models. Mathematical notations are clearly defined and explained throughout, making this book an invaluable resource for both newcomers and seasoned researchers in the field.
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