The accounting profession has undergone significant technological and operational change over the past fifteen years, yet the early-career experience of staff accountants has remained comparatively unchanged. This article examines the changing balance of power in the accounting labor market, where historically low unemployment, retirements, a constrained talent pipeline, and persistent first-year turnover have increased the importance of retaining young accountants. It argues that compensation and workload alone do not explain early-career departures. Lack of meaningful feedback, mentorship, professional development, and a sense of connection to the purpose of the work can contribute substantially to turnover. The article also considers the impact of artificial intelligence and automation on entry-level accounting, contending that these technologies can increase the value and capabilities of young professionals rather than simply eliminate junior roles. Firms that use automation to accelerate staff development, while providing structured mentorship, timely feedback, and individualized development plans, may be better positioned to retain talent and build future leadership capacity. The article concludes that the talent shortage presents firms with an opportunity to reconsider how they develop and engage young accountants, emphasizing that relatively simple management practices can become important components of a long-term retention strategy.
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
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