Real Customization or Just Marketing: Are Customized Versions of Chat GPT Useful?
Eduardo C. Garrido-Merch\'an, Jose L. Arroyo-Barrig\"uete, Francisco, Borr\'as-Pala, Leandro Escobar-Torres, Carlos Mart\'inez de Ibarreta, Jose, Mar\'ia Ortiz-Lozano, and Antonio Rua-Vieites

TL;DR
This study evaluates the usefulness of customized GPT models for educational purposes, finding they alter communication style and improve specific task responses but do not significantly outperform standard ChatGPT-4 Turbo.
Contribution
It demonstrates the practical effects of fine-tuning GPT models for a specific educational context and compares their performance to the uncustomized version.
Findings
Customized GPTs change communication style to be more relatable.
They provide better responses for specific tasks with context access.
No significant performance difference with ChatGPT-4 Turbo.
Abstract
Large Language Models (LLMs), as the case of OpenAI ChatGPT-4 Turbo, are revolutionizing several industries, including higher education. In this context, LLMs can be personalized through a fine-tuning process to meet the student demands on every particular subject, like statistics. Recently, OpenAI has launched the possibility to fine-tune their model with a natural language web interface, enabling the possibility to create customized GPT version deliberately conditioned to meet the demands of a specific task. The objective of this research is to assess the potential of the customized GPTs that have recently been launched by OpenAI. After developing a Business Statistics Virtual Professor (BSVP), tailored for students at the Universidad Pontificia Comillas, its behavior was evaluated and compared with that of ChatGPT-4 Turbo. The results lead to several conclusions. Firstly, a…
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Taxonomy
TopicsE-Learning and Knowledge Management · Explainable Artificial Intelligence (XAI) · AI in Service Interactions
MethodsMulti-Head Attention · Attention Is All You Need · Cosine Annealing · Adam · Attention Dropout · Discriminative Fine-Tuning · Weight Decay · Layer Normalization · Dense Connections · Linear Warmup With Cosine Annealing
