Comparative Evaluation of Fine-Tuned and Standard Language Models in Emulating Living Historical Figures: A Detailed Study Proposal

Abstract

The contemporary growth spurt in artificial intelligence has engendered an opportunity to explore evermore complex applications of language models. This research proposal outlines an in-depth comparative study aimed at investigating the proficiency of fine-tuned language models against standard general-purpose transformer (GPT) models. Specifically, we aim to emulate dialogues characteristic of selected, living historical figures. With a primary focus on accuracy, authenticity, and reliability, we propose an innovative "biographimetric" approach that correlates psychometric properties of AI-generated responses with biographical data. This study anticipates not only shedding light on the comparative potential of customized and standard language models but also developing a comprehensive evaluation matrix for such models. Furthermore, we aim to touch upon the array of practical applications, ethical issues, and potential impacts on various domains, including archival studies, AI ethics, and sectors such as education and entertainment. Ultimately, our research could contribute significantly to understanding the boundaries and possibilities of AI language models in capturing and replicating unique human thought processes.