  Interest in Large Language Models (LLMs) has increased drastically since the
emergence of ChatGPT and the outstanding positive societal response to the ease
with which it performs tasks in Natural Language Processing (NLP). The triumph
of ChatGPT, however, is how it seamlessly bridges the divide between language
generation and knowledge models. In some cases, it provides anecdotal evidence
of a framework for replicating human intuition over a knowledge domain. This
paper highlights the prevailing ideas in NLP, including machine translation,
machine summarization, question-answering, and language generation, and
compares the performance of ChatGPT with the major algorithms in each of these
categories using the Spontaneous Quality (SQ) score. A strategy for validating
the arguments and results of ChatGPT is presented summarily as an example of
safe, large-scale adoption of LLMs.
