Summary of Unveiling the Competitive Dynamics: a Comparative Evaluation Of American and Chinese Llms, by Zhenhui Jiang et al.
Unveiling the Competitive Dynamics: A Comparative Evaluation of American and Chinese LLMs
by Zhenhui Jiang, Jiaxin Li, Yang Liu
First submitted to arxiv on: 9 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper provides a comprehensive comparative evaluation of American and Chinese Large Language Models (LLMs) in both English and Chinese contexts, proposing an evaluation framework that assesses models’ natural language proficiency, disciplinary expertise, and safety and responsibility. The study finds GPT 4-Turbo leading in English contexts and Ernie-Bot 4 in Chinese contexts, highlighting disparities across languages and tasks. The research presents the current LLM competition landscape, offering insights for policymakers and businesses on strategic LLM investments and development. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper compares big language models from America and China to see how well they work in different languages and tasks. It uses a special framework to evaluate these models, looking at things like how good they are with language, how smart they are about specific topics, and whether they’re safe to use. The results show that some American models do better than others in English, while Chinese models perform better in Chinese. This highlights the importance of understanding different languages and cultures when making these models. The study also shows what it means for businesses and governments to invest in these models and how they can be used. |
Keywords
» Artificial intelligence » Gpt
