Exploring the technical path of knowledge service in Chinese STM journals:A case study of Consensus.app
[Purposes]This paper aims to explore new pathways and insights for knowledge services in Chinese STM journals in the era of large language models 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 of 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,with a summary of its potential advantages and disadvantages.[Findings]The corpus of the Consensus.app platform,based on the abstract information from the Semantic Scholar paper database,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 via direct data-supported conclusions for different queries and quick access to snapshot information of relevant literature,to help users make rapid decisions.[Conclusions]Consensus.app partially addresses the lack of accuracy and evidence chain in large language model responses.It also provides more diverse scenarios for the widespread and efficient application of scientific journals in the era of large language models.It demonstrates a new approach to integrate large language models into knowledge repositories for further knowledge services for STM journals.In the new era,the STM journal community needs to attach great importance to data quality development,cross-disciplinary collaboration,and copyright improvements,and it also needs to embrace the trend in the era of large models to move towards the"AI+"era of STM journals.
Large language modelSTM journalKnowledge serviceArtificial intelligence
NETL
NSTL
万方数据
