LEADER 03915nam 22005535 450 001 9911015683903321 005 20250904105405.0 010 $a9783031857478$b(electronic bk.) 010 $z9783031857461 024 7 $a10.1007/978-3-031-85747-8 035 $a(MiAaPQ)EBC32196111 035 $a(Au-PeEL)EBL32196111 035 $a(CKB)39586140800041 035 $a(DE-He213)978-3-031-85747-8 035 $a(OCoLC)1528361598 035 $a(EXLCZ)9939586140800041 100 $a20250704d2025 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aEnhancing LLM Performance $eEfficacy, Fine-Tuning, and Inference Techniques /$fedited by Peyman Passban, Andy Way, Mehdi Rezagholizadeh 205 $a1st ed. 2025. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2025. 215 $a1 online resource (279 pages) 225 1 $aMachine Translation: Technologies and Applications,$x2522-803X ;$v7 311 08$aPrint version: Passban, Peyman Enhancing LLM Performance Cham : Springer,c2025 9783031857461 327 $aIntroduction and Fundamentals -- SPEED: Speculative Pipelined Execution for Efficient Decoding -- Efficient LLM Inference on CPUs -- KronA: Parameter-Efficient Tuning with Kronecker Adapter -- LoDA: Low-Dimensional Adaptation of Large Language Models -- Sparse Fine-Tuning for Inference Acceleration of Large Language Models -- TCNCA: Temporal CNN with Chunked Attention for Efficient Training on Long Sequences -- Class-Based Feature Knowledge Distillation -- On the Use of Cross-Attentive Fusion Techniques for Audio-Visual Speaker Verification -- An Efficient Clustering Algorithm for Self-Supervised Speaker Recognition -- Remaining Issues for AI. 330 $aThis book is a pioneering exploration of the state-of-the-art techniques that drive large language models (LLMs) toward greater efficiency and scalability. Edited by three distinguished experts?Peyman Passban, Mehdi Rezagholizadeh, and Andy Way?this book presents practical solutions to the growing challenges of training and deploying these massive models. With their combined experience across academia, research, and industry, the authors provide insights into the tools and strategies required to improve LLM performance while reducing computational demands. This book is more than just a technical guide; it bridges the gap between research and real-world applications. Each chapter presents cutting-edge advancements in inference optimization, model architecture, and fine-tuning techniques, all designed to enhance the usability of LLMs in diverse sectors. Readers will find extensive discussions on the practical aspects of implementing and deploying LLMs in real-world scenarios. The book serves as a comprehensive resource for researchers and industry professionals, offering a balanced blend of in-depth technical insights and practical, hands-on guidance. It is a go-to reference book for students, researchers in computer science and relevant sub-branches, including machine learning, computational linguistics, and more. 410 0$aMachine Translation: Technologies and Applications,$x2522-803X ;$v7 606 $aMachine learning 606 $aNatural language processing (Computer science) 606 $aMachine Learning 606 $aNatural Language Processing (NLP) 615 0$aMachine learning. 615 0$aNatural language processing (Computer science) 615 14$aMachine Learning. 615 24$aNatural Language Processing (NLP). 676 $a006.31 700 $aPassban$b Peyman$01833750 701 $aWay$b Andy$01314322 701 $aRezagholizadeh$b Mehdi$01833751 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 912 $a9911015683903321 996 $aEnhancing LLM Performance$94408710 997 $aUNINA