Sep 2026· Communications in Humanities Research· 0 citations
Abstract
As artificial intelligence, big data, algorithmic recommendation and short-video technology continue to develop rapidly, the news industry is changing dramatically in the process of digitalisation and will alter its traditional model of news creation and dissemination. This paper explores the current situation of digital technology application in newsrooms, identifies the main problems that have arisen from the changes, and puts forward new ideas for utilising digital technologies in news production and dissemination. Through a systematic review of literature and comparative case studies of top news organisations around the world, it has been found that although digital technology has promoted improvements in production efficiency and expanded the spread of news content, serious problems have also arisen, such as content homogeneity due to algorithmic templates, the formation of information cocoons through personalised recommendations, limited use of technology solely at the distribution stage, and ethical issues caused by artificial intelligence and fake news. Given the circumstances mentioned above, the four directions of innovation proposed in this paper are as follows: A model of human-AI collaborative production; optimised algorithmic distribution with public interest weightings; diversified multi-platform dissemination strategies; and the construction of transparent AI ethics governance frameworks. The above results provide new material for discussions on the future of journalism in the era of generative artificial intelligence and offer some suggestions for news organizations in balancing the efficiency of technology with ethical journalism.
GAOKAO-Bench is introduced, an intuitive benchmark that employs questions from the Chinese GAOKAO examination as test samples, including both subjective and objective questions that contribute a robust evaluation benchmark for future large language models and offers valuable insights into the advantages and limitations of such models.
Xiaotian Zhang, Chun-yan Li, Yi Zong et al.· arXiv.org· 216 citations· ⚡17
Empirically, PRISM reduces the end-to-end time for data selection and model tuning to just 30% of conventional pipelines, and achieves this efficiency while simultaneously enhancing performance, surpassing models fine-tuned on the full dataset across eight multimodal and three language understanding benchmarks.
Jinhe Bi, Yifan Wang, Danqi Yan et al.· arXiv.org· 73 citations· ⚡4
The method, ECCOLA, is presented, which aims at making the high-level AI ethics principles more practical, making it possible for developers to more easily implement them in practice.
Ville Vakkuri, Kai-Kristian Kemell, P. Abrahamsson· EUROMICRO Conference on Soft...· 64 citations· ⚡6
The goal is to not only refine the accuracy of the LLM-based tool but also to underscore its potential in streamlining the software development lifecycle through proactive code improvement and education.
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This paper designs Markov decision processes (MDPs) for different combinatorial problems and proposes to train conditional GFlowNets to sample from the solution space and demonstrates that GFlowNet policies can efficiently find high-quality solutions.
Dinghuai Zhang, H. Dai, Esmeralda S. Whitammer et al.· Advances in Neural Informati...· 59 citations· ⚡8
An empirical study on the current state of practice in artificial intelligence ethics is conducted by means of a multiple case study of five case companies, which indicates a gap between research and practice in the area.
Ville Vakkuri, Kai-Kristian Kemell, Joni Kultanen et al.· arXiv.org· 56 citations· ⚡6
The professor of physics and inaugural director of the NSF AI Institute for Artificial Intelligence and Fundamental Interactions will lead LNS and continue his research in particle physics.