Undergrads Are All You Have

TL;DR

This paper introduces GPT-UGRD, a cost-effective alternative to Lama-based models for NLP tasks, outperforming some in accuracy and ease of use, while also addressing social implications of replacing undergraduates.

Contribution

The paper presents GPT-UGRD, a new language model that is cheaper, easier to train, and performs comparably or better than Lama models for NLP tasks.

Findings

GPT-UGRD matches or exceeds Lama models in NLP performance.

GPT-UGRD is cheaper and easier to train and operate.

The paper discusses social implications of replacing undergraduates with AI models.

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.