دانلود کتاب پردازش زبان طبیعی و مدل‌های زبانی بزرگ – نظریه، کدهای عملی و مطالعات موردی

In recent years, Natural Language Processing (NLP) has attracted significant attention. On one hand, NLP has made great progress. It can now solve many real-world problems with good performance, such as machine translation, dialogue systems, question answering, automatic text summarization, and article writing. On the other hand, people’s requirements and expectations for NLP have become higher. This is especially true since 2022, when Large Language Models (LLMs) came into being. This important breakthrough not only changed people’s traditional views on NLP but also made people hope that LLMs can help them complete various complex tasks. To be honest, NLP was just an ordinary research field in the past. But now, it has quickly developed into a popular discipline. It has even become a required core course in AI programs and departments at most universities. Obviously, NLP has entered its best development period. The sustainable development of NLP cannot be achieved without a large number of beginners devoting themselves to this field. However, the current state of NLP has both positive and negative impacts on beginners. On the positive side, the boundaries between different data modalities have become increasingly blurred, and the techniques used across various NLP applications have grown more similar. This convergence has made the learning process easier for beginners. Besides, open source has become a key trend in the NLP community. The community now provides a wealth of open resources, including datasets, algorithms, tools, and pre-trained models, which greatly benefits those who are new to the field. On the negative side, the large number of algorithms and models brings unique challenges to beginners. They have to master both traditional NLP methods and the basic theories of LLMs. What’s more, NLP and LLMs are highly practical fields; beginners need to learn essential hands-on skills, such as data processing, model deployment, parameter configuration and optimization, and how to use related platforms. It was exactly these challenges that inspired us to write a book to meet these specific needs. Fortunately, when the Baidu PaddlePaddle team contacted me about co-writing a book on NLP and LLMs that integrates theory with practice, I agreed right away. We first completed a Chinese version of the book. Shortly after finishing it, editors from Springer reached out to us to discuss translating the book into English, with the goal of making its content available to readers around the world. We started the translation and rewriting immediately and expanded some chapters. This book is written under the assumption that readers already have basic knowledge of NLP theories and methods. Therefore, theoretical content is just briefly introduced, and our main focus is on step-by-step practical implementation processes, such as data processing, structure construction, and model optimization. In terms of structure, it begins by introducing fundamental NLP theories and methods. Next, it explores prac-tical implementation approaches for different NLP applications. Finally, it focuses on LLM-based generation systems. Every piece of code provided in the book has undergone strict testing and is hosted on the Baidu AI Studio Community as Jupyter Notebooks. By following the instructions in the book, readers can directly access the free GPU computing resources provided by the AI Studio Xinghe Community to compile and run the code online—this greatly simplifies the process of practicing and learning. This book was co-authored by Dr. Zhao Yang and the PaddlePaddle team, whose expertise and support were instrumental in its completion. The book project also received funding from the Publishing Center of the University of Chinese Academy of Sciences. We would like to express our gratitude to Ms. Tian Chenchen, Prof. Xiao Jun, and Ms. Qu Xiaochun from the University of Chinese Academy of Sciences for their invaluable assistance; to Prof. Zhao Tiejun, Prof. Wang Houfeng, Prof. Wang Xiaojie, Prof. Huang Minlie, and Prof. Zhang Jiajun for their constructive suggestions on the book’s contents; to Dr. Xiang Lu from the Institute of Automation, Chinese Academy of Sciences for invaluable suggestions; and to Editor Chang Lanlan from Springer for her consistent support throughout the publishing process.