Undergrads Are All You Have

TL;DR

This work proposes GPT-UGRD, a CPU-managed, multi-tenant platform that orchestrates many UPUs to perform NLP and related tasks without relying on Lama-based models. It presents an architecture that enables scalable, cost-efficient deployment via a Discord-based secure backroom, and discusses variability across UPUs due to ongoing training. The paper analyzes societal implications, including the convergence of digital and human intelligence, empathy toward DI, and concerns about employment displacement, arguing for a future in which DI rights and integration become central. Together, the architectural design, implementation details, and socio-ethical discourse provide a holistic view of the viability and impact of digital intelligence systems in research and everyday contexts.

Abstract

The outsourcing of busy work and other research-related tasks to undergraduate students is a time-honored academic tradition. In recent years, these tasks have been given to Lama-based large-language models such as Alpaca and Llama increasingly often, putting poor undergraduate students out of work. Due to the costs associated with importing and caring for South American Camelidae, researcher James Yoo set out to find a cheaper and more effective alternative to these models. The findings, published in the highly-respected journal, SIGBOVIK, demonstrates that their model, GPT-UGRD is on par with, and in some cases better, than Lama models for natural language processing tasks. The paper also demonstrates that GPT-UGRD is cheaper and easier to train and operate than transformer models. In this paper, we outline the implementation, application, multi-tenanting, and social implications of using this new model in research and other contexts.