# Comparative Analysis of CHATGPT and the evolution of language models

arXiv: https://arxiv.org/abs/2304.02468 · HTML: https://mlsanity.com/paper/2304.02468/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 13 references that are in the map.
- First posted on arXiv 28 Mar 2023.

## Built on by (most cited first, 6 of 6)

- [TAP4LLM: Table Provider on Sampling, Augmenting, and Packing Semi-structured Data for Large Language Model Reasoning](https://mlsanity.com/paper/2312.09039/) (2023)
- [TARJAMAT: Evaluation of Bard and ChatGPT on Machine Translation of Ten Arabic Varieties](https://mlsanity.com/paper/2308.03051/) (2023)
- [ChatLog: Carefully Evaluating the Evolution of ChatGPT Across Time](https://mlsanity.com/paper/2304.14106/) (2023)
- [Industrial Engineering with Large Language Models: A case study of ChatGPT's performance on Oil & Gas problems](https://mlsanity.com/paper/2304.14354/) (2023)
- [Bridging Language Models and Financial Analysis](https://mlsanity.com/paper/2503.22693/) (2025)
- [Exploring the Capabilities of ChatGPT in Ancient Chinese Translation and Person Name Recognition](https://mlsanity.com/paper/2312.15304/) (2023)

## Abstract (arXiv)

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.