Knowledge-augmented Methods for Natural Language Processing - Meng Jiang Bill Yuchen Lin Shuohang Wang Yichong Xu Wenhao Yu Chenguang Zhu Kartoniert (TB)

Over the last few years natural language processing has seen remarkable progress due to the emergence of larger-scale models better training techniques and greater availability of data. Examples of these advancements include GPT-4 ChatGPT and other pre-trained language models. These models are capable of characterizing linguistic patterns and generating context-aware representations resulting in high-quality output. However these models rely solely on input-output pairs during training and therefore struggle to incorporate external world knowledge such as named entities their relati ons common sense and domain-specific content. Incorporating knowledge into the training and inference of language models is critical to their ability to represent language accurately. Additionally knowledge is essential in achieving higher levels of intelligence that cannot be attained through statistical learning of input text patterns alone. In this book we will review recent developmentsin the field of natural language processing specifically focusing on the role of knowledge in language representation. We will examine how pre-trained language models like GPT-4 and ChatGPT are limited in their ability to capture external world knowledge and explore various approaches to incorporate knowledge into language models. Additionally we will discuss the significance of knowledge in enabling higher levels of intelligence that go beyond statistical learning on input text patterns. Overall this survey aims to provide insights into the importance of knowledge in natural language processing and highlight recent advances in this field.