EAN: 9789819707492

KNOWLEDGE-AUGMENTED METHODS FOR NATURAL LANGUAG...

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 relations  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.

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