Sep 2026· The journal of classics teaching· 11 references
Abstract
Abstract This work focuses on an educational project for third-year students in Italian classical high schools, integrating generative artificial intelligence (AI) into the study of Latin. Using tools like ChatGPT, the project explores innovative methods to enhance linguistic and cultural understanding while promoting ethical AI use. Generative AI enables the study and learning of Latin language and culture through new solutions. This educational activity encourages critical thinking and dialogue on translational and linguistic choices, improving the ability to analyse Latin texts, formulate sentences in Latin, and better understand basic and everyday grammar. Creativity and active use of Latin are also emphasised. Collaborative and individual working methods facilitate the exploration of AI’s advantages and limitations, enriching traditional pedagogy with a modern, interactive approach. The project consists of five modules, each exploring how to integrate AI into the current teaching program for third-year students. Specifically, the modules aim to enhance students’ capabilities in analysing and translating Latin texts, highlighting differences between human-generated and AI-generated results. Students critically compare these methods, refining their grammatical and syntactical understanding. Collaboration and cooperation among students are also emphasised. For example, they use AI to create dialogues in Latin inspired by Plautus’s conversational style. The impact of the activity is assessed through an analysis of students’ translations, ethical reflections, and methodological insights. In this paper, we will share our experience with this experiment, which aims, with the help of technology, to present Latin as a dynamic and living language, underscoring the enduring relevance of the classical tradition in contemporary education.
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...
Xiaotian Zhang, Chun-yan Li, Yi Zong et al.· arXiv.org· 216 citations· ⚡17
This work proposes a HALO that directly maximizes the utility of generations instead of maximizing the log-likelihood of preferences, and suggests that the best loss depends on the inductive biases most appropriate for a given setting, an oft-overlooked consideration.
This work investigates the possibilities of using LLMs in a resume screening setting via a document retrieval framework that simulates job candidate selection and finds that the MTEs are biased, significantly favoring White-associated names in 85% of cases and female-associated names in only 11.1% of cases.
This work shows that orders of magnitude enhancement in performance could be obtained by a combination of hardware improvements and tight quantum-HPC integration and introduces high-performance architectures for quantum-probabilistic computing with custom-designed accelerators to tackle today's industry-scale classical...
Masoud Mohseni, Artur Scherer, K. Johnson et al.· arXiv.org· 121 citations· ⚡9
This paper presents a comprehensive overview of the Ultralytics YOLO family, emphasizing architectural evolution, benchmarking, deployment, and emerging directions from YOLOv5 through YOLO27, and examines detection, segmentation, depth, classification, pose, oriented detection, tracking, export, quantization, and deplo...
A novel threat is unveiled in which attackers steer the RAG system's response by injecting malicious passages into its knowledge base, enabling the attacker to steer the response without altering the user input or modifying the RAG weights.
Jiaqi Xue, Meng Zheng, Yebowen Hu et al.· arXiv.org· 109 citations· ⚡8
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