Research on methods and applications of question answering system in the context of ChatGPT

Abstract

In the 21st century, there has been a growing importance placed on the "body" of artificial intelligence, particularly as it relates to language processing. Researchers have developed various machine learning models with a focus on language understanding, including Large Language Model (LLM), Bidirectional Encoder Representation from Transformers (BERT), and Natural Language Processing (NLP). These models have led to the development of numerous applications, such as ChatGPT-3.5, which has recently gained widespread attention. In addition to ChatGPT, other applications have also benefited from these language processing models, including Question Answering Systems (QAS). This paper will examine three QAS that have been enhanced by the context of ChatGPT, discuss the relevant applications, and analyze these different applications in order to predict future trends in this field. One notable QAS is OpenAI's GPT-3-powered AI that can answer questions about any topic. This application leverages the capabilities of GPT-3 to provide accurate and informative responses to a wide range of questions. Another QAS is IBM's Watson, which utilizes natural language processing and machine learning algorithms to understand and respond to user queries. Watson has been used in various industries, including healthcare, finance, and retail. A third QAS is Google's BERT-based system, which uses pre-trained language models to improve its responses to user queries. This system has been integrated into Google Search and other products, allowing users to receive more precise and relevant search results. Overall, the development of these QAS and other language processing applications marks an exciting period of progress in the field of artificial intelligence. As researchers continue to refine these models and explore new applications, we can expect to see even more advanced and sophisticated language processing systems emerge in the future.