Abstract In the chapter we consider Information Extraction approaches that automatically identify structured information in text documents and comprise a set of tasks. The Text Classification task assigns document to one or more pre-defined content categories classes. This includes many subtasks such as language identification, sentiment analysis, etc. Word Sense Disambiguation attaches predefi...

Abstract This chapter discusses Foundation Models for Text Generation. includes systems Document Retrieval, which accept a query and return an ordered list of text documents from document collection, often evaluating the similarity embeddings to retrieve relevant passages. Question Answering are given natural language question must provide answer, usually in language. Machine Translation models...

Abstract During pre-training, a Foundation Model is trained on an extensive collection of documents and learns the distribution words in correct fluent language. In this chapter, we investigate knowledge acquired by PLMs larger Models. We first discuss application Models to specific benchmarks test large number areas examine if models are able derive conclusions from content. Another group test...

due to the heterogeneous nature of granular soils and the involvement of many effective parameters in the geotechnical behavior of soil-foundation systems, the accurate prediction of shallow foundation settlements on cohesionless soils is a complex engineering problem. in this study, three new evolutionary-based techniques, including evolutionary polynomial regression (epr), classical genetic p...

This open access book gives a deep overview of pre-trained language foundation models (BERT, GPT) and state-of-the-art for wide range NLP tasks

Foundation Models (FMs) are models trained on large corpora of data that, at very scale, can generalize to new tasks without any task-specific finetuning. As these continue grow in size, innovations push the boundaries what do language and image tasks. This paper aims understand an underexplored area FMs: classical like cleaning integration. a proof-of-concept, we cast five integration as promp...