3 research outputs found
A study on the impact of social demographic factors on financial literacy among final year degree students in UiTM Seremban 3 / Hanif Imran Ahmad Rushdy and Muhammad Zaid Akmal Zainal
No full textThis study investigates the impact of social demographic factor on financial literacy among final year degree students in UiTM Seremban 3. This research used a convenience sampling involving 311 final year degree students in UiTM Seremban 3. This research is intended to establish the vital relationship between independent variables and dependent variables. This study indicates that the social demographic profile of gender, faculty, parental education and residency in finding the relationship with financial literacy. Financial literacy under this research is been viewed under three factors which are financial attitude, parental influence and financial knowledge. In this study, the survey approach of distributing online questionnaire was used to gather information and data in completing the discussion. Hence, the study of this research found that, residency and parental education plays a huge role in the determinant of student’s financial literacy. Therefore, this study suggested that more financial literacy programs can be conducted to younger students so that the student will have early awareness on the important of financial management
Global PIQA: Evaluating Physical Commonsense Reasoning Across 100+ Languages and Cultures
No full textTo date, there exist almost no culturally-specific evaluation benchmarks for large language models (LLMs) that cover a large number of languages and cultures. In this paper, we present Global PIQA, a participatory commonsense reasoning benchmark for over 100 languages, constructed by hand by 335 researchers from 65 countries around the world. The 116 language varieties in Global PIQA cover five continents, 14 language families, and 23 writing systems. In the non-parallel split of Global PIQA, over 50% of examples reference local foods, customs, traditions, or other culturally-specific elements. We find that state-of-the-art LLMs perform well on Global PIQA in aggregate, but they exhibit weaker performance in lower-resource languages (up to a 37% accuracy gap, despite random chance at 50%). Open models generally perform worse than proprietary models. Global PIQA highlights that in many languages and cultures, everyday knowledge remains an area for improvement, alongside more widely-discussed capabilities such as complex reasoning and expert knowledge. Beyond its uses for LLM evaluation, we hope that Global PIQA provides a glimpse into the wide diversity of cultures in which human language is embedded.See §A for author list. Global PIQA would not be possible without the efforts of all of the authors. Wealso thank several anonymous contributors who preferred not to be authors on this paper. The research of Yolanda Xavier is supported by Portuguese national funding through the FCT– Portuguese Foundation for Science and Technology, I.P. as part of the project UID/3213/2025– Linguistics Research Centre of NOVA University Lisbon (CLUNL) and by the Doctoral Grant (FCT PhD grant) number 2022.13977.BD from the same funder. Group 0025 is supported by the following grants: CLARIN-PL (POIR.04.02.00-00C002/19, FENG.02.04-IP.040004/24, 2024/WK/01), DARIAH-PL (POIR.04.02.00-00-D006/20, KPOD.01.18-IW.03-0013/23). Annika Simonsen was funded by the European Commission under grant agreement no. 101135671. CEB has been partially funded by the German ministry for education and research (BMBF) through the TRAILS project (grant number 01IW24005). Group 0070 is supported by funding from King Abdullah University of Science and Technology (KAUST)- Center of Excellence for Generative AI, under award number 5940. Group 0079 would like to thank Mr. Sudhir R. Narayana for help with correction and verification of items in their dataset. Sina Ahmadi gratefully acknowledges support from the University of Zurich (UZH) Postdoc Grant (reference number 269093). Group 0133 would like to thank the MbazaNLP community, including students from the University of Rwanda, School of Art and Languages. We would also like to thank Yonatan Bisk for useful insights into the original PIQA dataset
