Subjects: Agriculture, Forestry,Livestock & Aquatic Products Science >> Other Disciplines of Agriculture, Forestry,Livestock & Aquatic Products Science submitted time 2024-06-26 Cooperative journals: 《农业图书情报学报》
Abstract： Purpose/Significance This article is aimed to explore the impact of artificial intelligence generation technologies such as large language models (LLMs) on users’ information retrieval behavior and to suggest ideas for information retrieval systems and information resource construction. In this way, it provides insights into and references for the future establishment of the artificial intelligence generated content (AIGC) information platform with Chinese characteristics as well as the information literacy education system. Method/Process In the field of library intelligence, with the wide application of AI technology in information service work, LLMs represented by ChatGPT have also become a hot topic of discussion. Taking the booming development of LLMs such as ChatGPT as background, we analyzed the impact of the increasing popularity of this technology on information retrieval systems and user retrieval behavior from the perspective of user information behavior by combining the technical features of LLMs with the characteristics of existing products. Literature survey and empirical analysis were used. Results/Conclusions The use of LLMs as information retrieval systems has unparalleled advantages over traditional products. These advantages include the ability to understand and process natural language queries, generate relevant and context-specific responses, and interact with users in a more human-like way. The application of LLMs in information retrieval systems has the potential to transform the way users search for information, influence the underlying logic, action priorities, and retrieval expectations of user information retrieval behavior. However, the existing shortcomings of LLMs in terms of reliability and accuracy still make it difficult for them to replace traditional information retrieval methods immediately. Language models may not always provide accurate and reliable answers, especially when dealing with complex or domain-specific queries. Additionally, LLMs may struggle to understand and process contextual information effectively, leading to limitations in their ability to extract relevant and context-aware insights. It is recommended to pay attention to this technology in the construction of information retrieval systems and information resources, and to explore the combination of LLMs and information services in order to cope with the changes in future user information needs and to further make full use of the value of existing information resources. Limited by the lack of expertise in the field of AI and the fact that LLMs are not yet widely used in practice in China, the research findings are only a reflection and exploration of the impact of LLMs on users’ information behavior.
submitted time 2024-04-03 Cooperative journals: 《农业图书情报学报》
Abstract： [Purpose/Significance] This article is aimed to explore the impact of artificial intelligence generation technologies such as large language models (LLMs) on users' information retrieval behavior and to suggest ideas for information retrieval systems and information resource construction. In this way, it provides insights into and references for the future establishment of the artificial intelligence generated content (AIGC) information platform with Chinese characteristics as well as the information literacy education system. [Method/Process] In the field of library intelligence, with the wide application of AI technology in information service work, LLMs represented by ChatGPT have also become a hot topic of discussion. Taking the booming development of LLMs such as ChatGPT as background, we analyzed the impact of the increasing popularity of this technology on information retrieval systems and user retrieval behavior from the perspective of user information behavior by combining the technical features of LLMs with the characteristics of existing products. Literature survey and empirical analysis were used. [Results/Conclusions] The use of LLMs as information retrieval systems has unparalleled advantages over traditional products. These advantages include the ability to understand and process natural language queries, generate relevant and context-specific responses, and interact with users in a more human-like way. The application of LLMs in information retrieval systems has the potential to transform the way users search for information, influence the underlying logic, action priorities, and retrieval expectations of user information retrieval behavior. However, the existing shortcomings of LLMs in terms of reliability and accuracy still make it difficult for them to replace traditional information retrieval methods immediately. Language models may not always provide accurate and reliable answers, especially when dealing with complex or domain-specific queries. Additionally, LLMs may struggle to understand and process contextual information effectively, leading to limitations in their ability to extract relevant and context-aware insights. It is recommended to pay attention to this technology in the construction of information retrieval systems and information resources, and to explore the combination of LLMs and information services in order to cope with the changes in future user information needs and to further make full use of the value of existing information resources. Limited by the lack of expertise in the field of AI and the fact that LLMs are not yet widely used in practice in China, the research findings are only a reflection and exploration of the impact of LLMs on users' information behavior.
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