Exploring the Technical Path of Knowledge Service in Chinese STM journals: a case study of Consensus.app

Subjects: Digital Publishing >> Internet Journals submitted time 2024-04-25

LIU Hongxia WANG Lilei Shen Xibin HUO Yongfeng LIU Bing

Abstract： Purposes To explore new pathways and insights for knowledge services in Chinese STM journals in the era of large language model technology by analyzing the operational model and technical implementation of the novel knowledge service platform, Consensus.app. Methods By combining literature review and online empirical research, this study analyzed the functions, features, and manifestations of knowledge services provided by the Consensus.app platform. In this paper, the technical implementation methods employed by the platform and its role in promoting knowledge acquisition from scientific literature were investigated, while its potential advantages and disadvantages were summarized too. Results The corpus of the Consensus.app platform was built by collecting abstract information from the Semantic Scholar paper database, and it employs various artificial intelligence technologies, including natural language processing, machine learning, and information retrieval. By extracting key information from research papers and creating a vectorized knowledge database, Consensus.app utilizes OpenAI’s interface to retrieve relevant information from the knowledge base based on user queries and provides summarized conclusions as feedback to users. The platform offers highly personalized interactions, directly providing data-supported conclusions for different queries and enabling quick access to snapshot information of relevant literature, assisting users in making rapid decisions. Conclusions The emergence of Consensus.app partially addresses the issues of accuracy and evidence chain that are lacking in large-scale language model responses. It also provides more diverse scenarios for the widespread and efficient application of scientific journals in the era of large-scale language models. It demonstrates a new pathway for integrating large language models to construct knowledge repositories and provide knowledge services for STM journals. In the new era, the STM journal community needs to attach great importance to data quality construction, cross-disciplinary collaboration, copyright improvements, actively join large-scale databases, embrace the trend of the era of large models, and embrace the AI+ era of STM journals.

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