Efficacy of Chat GPT Correlations vs. Co-occurrence Networks in Deciphering Chinese History

We propose to examine the writing of the founder of the Chinese Communist Party, Chen Duxiu (1879-1942), by using the innovative AI technique ChatGPT. We intend to leverage ChatGPT to detect change in Chen’s thinking about politics, specifically on his thoughts about capitalism and communism. The central objective is to compare the efficacy of our 2021 work on co-occurrence network methodology with the novel ChatGPT approach. We seek the best method to trace Chen’s ideological evolution – from an early advocate of Western-style democracy to a proponent of Marxism and communism, and to a final reckoning at the end of his life. Our tools include ChatGPT, LLaMA, GPT-API, and Python. Early finding is that the concept (topic-modeling) by GPT is accurate but constrained by word limits. We will need more time to do a finer comparison. Our finding is that Llamas with 2x7 billion parameters model is not adequate in providing a summary, whereas ChatGPT-4 is strong is summarizing straight-forward analysis, but would need more prompt engineering to detect multiple and nuanced points of view. We offer two innovations in this paper: 1) the utilization of advanced AI methodologies grounded in extensive language models, as opposed to traditional statistical techniques that often relied on oversimplified assumptions, and 2) an extension of our analysis to sentences rather than individual words, allowing for richer contextual understanding.