Abstract
The proliferation of large language models represents a paradigm shift in the landscape of automated essay scoring (AES) systems, fundamentally elevating their accuracy and efficacy. This study presents an extensive examination of large language models, with a particular emphasis on the transformative influence of transformer-based models, such as BERT, mBERT, LaBSE, and GPT, in augmenting the accuracy of multilingual AES systems. The exploration of these advancements within the context of the Turkish language serves as a compelling illustration of the potential for harnessing large language models to elevate AES performance in in low-resource linguistic environments. Our study provides valuable insights for the ongoing discourse on the intersection of artificial intelligence and educational assessment.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Assessment Tools in Education
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
