This paper surveys different prompt engineering techniques used to improve the performance of large language models on various Natural Language Processing (NLP) tasks. It categorizes these techniques by NLP task, highlights their performance on different datasets, and discusses state-of-the-art methods for specific datasets. The survey covers 44 research papers exploring 39 prompting methods across 29 NLP tasks.
This article explores the application of XML Schema in AI systems and prompts. XML Schema provides a structured way to describe and validate data, making it an essential tool for AI systems that deal with data. The author discusses how XML Schema can be used to create and manage data in AI applications, such as speech recognition and natural language processing. The article also covers the benefits of using XML Schema in AI systems, including improved data consistency, interoperability, and security. Lastly, the author provides some examples of XML Schema usage in AI systems and discusses the future of XML Schema in AI technology.
