Unveiling the Competitive Dynamics: A Comparative Evaluation of American and Chinese LLMs

Zhenhui Jiang, Jiaxin Li, Yang Liu
5/9/2024
cs.CLcs.AI

No confident code implementation found for this paper.

Abstract

The strategic significance of Large Language Models (LLMs) in economic expansion, innovation, societal development, and national security has been increasingly recognized since the advent of ChatGPT. This study provides a comprehensive comparative evaluation of American and Chinese LLMs in both English and Chinese contexts. We proposed a comprehensive evaluation framework that encompasses natural language proficiency, disciplinary expertise, and safety and responsibility, and systematically assessed 16 prominent models from the US and China under various operational tasks and scenarios. Our key findings show that GPT 4-Turbo is at the forefront in English contexts, whereas Ernie-Bot 4 stands out in Chinese contexts. The study also highlights disparities in LLM performance across languages and tasks, stressing the necessity for linguistically and culturally nuanced model development. The complementary strengths of American and Chinese LLMs point to the value of Sino-US collaboration in advancing LLM technology. The research presents the current LLM competition landscape and offers valuable insights for policymakers and businesses regarding strategic LLM investments and development. Future work will expand on this framework to include emerging LLM multimodal capabilities and business application assessments.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. Any repos shown below are weak matches — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Cite this paper

@article{jiang2024unveiling,
  title  = {Unveiling the Competitive Dynamics: A Comparative Evaluation of American and Chinese LLMs},
  author = {Zhenhui Jiang and Jiaxin Li and Yang Liu},
  year   = {2024},
  eprint = {2405.06713},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2405.06713v2}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2405.06713.svg)](https://codeofpaper.com/paper/2405.06713)

Discussion