Comparative Analysis of CHATGPT and the evolution of language models
It provides a comparative analysis for NLP researchers and practitioners, but appears incremental as it benchmarks an existing model against established methods.
This paper compares ChatGPT's performance against major NLP algorithms in tasks like machine translation and question-answering using the Spontaneous Quality (SQ) score, and presents a validation strategy for safe LLM adoption.
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.