Foundation Models for Natural Language Processing: Pre-Trained Language Models Integrating Media (Paperback)
暫譯: 自然語言處理的基礎模型：整合媒體的預訓練語言模型（平裝本）

Paaß, Gerhard, Giesselbach, Sven

Foundation Models for Natural Language Processing: Pre-Trained Language Models Integrating Media (Paperback)
  • 出版商: Springer
  • 出版日期: 2023-05-24
  • 售價: $2,080
  • 貴賓價: 9.5 折 $1,976
  • 語言: 英文
  • 頁數: 436
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 3031231929
  • ISBN-13: 9783031231926
  • 相關分類: 人工智慧、Machine Learning、Text-mining

    • 立即出貨 (庫存=1)

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商品描述

This open access book provides a comprehensive overview of the state of the art in research and applications of Foundation Models and is intended for readers familiar with basic Natural Language Processing (NLP) concepts.

Over the recent years, a revolutionary new paradigm has been developed for training models for NLP. These models are first pre-trained on large collections of text documents to acquire general syntactic knowledge and semantic information. Then, they are fine-tuned for specific tasks, which they can often solve with superhuman accuracy. When the models are large enough, they can be instructed by prompts to solve new tasks without any fine-tuning. Moreover, they can be applied to a wide range of different media and problem domains, ranging from image and video processing to robot control learning. Because they provide a blueprint for solving many tasks in artificial intelligence, they have been called Foundation Models.

After a brief introduction to basic NLP models the main pre-trained language models BERT, GPT and sequence-to-sequence transformer are described, as well as the concepts of self-attention and context-sensitive embedding. Then, different approaches to improving these models are discussed, such as expanding the pre-training criteria, increasing the length of input texts, or including extra knowledge. An overview of the best-performing models for about twenty application areas is then presented, e.g., question answering, translation, story generation, dialog systems, generating images from text, etc. For each application area, the strengths and weaknesses of current models are discussed, and an outlook on further developments is given. In addition, links are provided to freely available program code. A concluding chapter summarizes the economic opportunities, mitigation of risks, and potential developments of AI.

 

 

商品描述(中文翻譯)

這本開放存取的書籍提供了有關基礎模型（Foundation Models）研究和應用的最新狀況的全面概述，適合對基本自然語言處理（Natural Language Processing, NLP）概念有一定了解的讀者。

近年來，為自然語言處理訓練模型開發了一種革命性的全新範式。這些模型首先在大量文本文件上進行預訓練，以獲取一般的語法知識和語義信息。然後，它們會針對特定任務進行微調，通常能以超越人類的準確度解決這些任務。當模型足夠大時，可以通過提示指令來解決新任務，而無需任何微調。此外，它們可以應用於各種不同的媒介和問題領域，從圖像和視頻處理到機器人控制學習。由於它們為解決許多人工智慧任務提供了藍圖，因此被稱為基礎模型。

在簡要介紹基本的NLP模型後，將描述主要的預訓練語言模型BERT、GPT和序列到序列的變壓器（transformer），以及自注意力（self-attention）和上下文敏感嵌入（context-sensitive embedding）的概念。接著，將討論改善這些模型的不同方法，例如擴展預訓練標準、增加輸入文本的長度或包含額外知識。然後，將介紹約二十個應用領域中表現最佳的模型概述，例如問題回答、翻譯、故事生成、對話系統、從文本生成圖像等。對於每個應用領域，將討論當前模型的優缺點，並展望未來的發展。此外，還提供了可自由獲取的程式碼連結。最後一章總結了人工智慧的經濟機會、風險緩解和潛在發展。

作者簡介

Dr. Gerhard Paaß is a Lead Scientist at the Fraunhofer Institute for Intelligent Analysis and Information Systems (IAIS). With a background in Mathematics, he is a recognized expert in the field of Artificial Intelligence, particularly in the area of Natural Language Processing. Dr. Paaß has previously worked at UC Berkeley in California and the University of Technology in Brisbane. He has served as reviewer and conference chair at various international conferences, including NeurIPS, CIKM, ECML/PKDD, ICDM, and KDD, where he regularly is a member of the program committee. Dr. Paaß has received a "best paper" award on probabilistic logic and is the author of about 70 publications for international conferences and journals. Recently, he authored the book "Artificial Intelligence: What's Behind the Technology of the Future?" (in German). He is currently involved in the creation of a computer center for Foundation Models. Besides experimental research on Foundation Models, he holds lectures for Deep Learning and Natural Language Understanding at the University of Bonn and in industry.
Sven Giesselbach is the leader of the Natural Language Understanding (NLU) team at the Fraunhofer Institute for Intelligent Analysis and Information Systems (IAIS), where he has specialized in Artificial Intelligence and Natural Language Processing. He and his team develop solutions in the areas of medical, legal and general document understanding, which in their core build upon Foundation Models. Sven Giesselbach is also part of the Competence Center for Machine Learning Rhine-Ruhr (ML2R), where he works as a research scientist and investigates Informed Machine Learning, a paradigm in which knowledge is injected into machine learning models, in conjunction with language modeling. He has published more than 10 papers on Natural Language Processing and Understanding, which focus on the creation of application-ready NLU systems and the integration of expert knowledge in various stages of the solution design. He led the development of the Natural Language Understanding Showroom, a platform for showcasing state-of-the-art Natural Language Understanding models. He regularly gives talks about NLU at summer schools, conferences and AI-Meetups.

作者簡介(中文翻譯)

德國的 Gerhard Paaß 博士是弗勞恩霍夫智能分析與資訊系統研究所 (IAIS) 的首席科學家。他擁有數學背景，是人工智慧領域的公認專家，特別是在自然語言處理方面。Paaß 博士曾在加州的加州大學伯克利分校和布里斯班的科技大學工作。他曾擔任多個國際會議的審稿人和會議主席，包括 NeurIPS、CIKM、ECML/PKDD、ICDM 和 KDD，並定期擔任程序委員會成員。Paaß 博士因其在概率邏輯方面的研究獲得了「最佳論文」獎，並且是約 70 篇國際會議和期刊出版物的作者。最近，他撰寫了《人工智慧：未來科技背後的秘密？》（德文）。他目前參與建立一個基於基礎模型的計算中心。除了對基礎模型的實驗研究外，他還在波恩大學和業界教授深度學習和自然語言理解的課程。

Sven Giesselbach 是弗勞恩霍夫智能分析與資訊系統研究所 (IAIS) 自然語言理解 (NLU) 團隊的負責人，專注於人工智慧和自然語言處理。他和他的團隊在醫療、法律和一般文件理解領域開發解決方案，這些解決方案的核心基於基礎模型。Sven Giesselbach 也是萊茵-魯爾機器學習能力中心 (ML2R) 的一部分，擔任研究科學家，研究知識注入機器學習模型的知情機器學習範式，並結合語言建模。他在自然語言處理和理解方面發表了超過 10 篇論文，重點在於創建應用就緒的 NLU 系統以及在解決方案設計的各個階段整合專家知識。他主導了自然語言理解展示平台的開發，該平台用於展示最先進的自然語言理解模型。他定期在暑期學校、會議和人工智慧聚會上發表有關 NLU 的演講。

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