Performance Comparison Analysis of Large Language Models Based on Objective Ability Testing and Subjective User Experience

At present, large language models have profoundly transformed many fields, including human-computer interaction, content creation, and code generation. They demonstrate astonishing, even human-like abilities in various tasks. At the same time, it has also brought about huge risks and social concerns, such as the existence of prejudice, the leakage of private data, and the abuse of false information dissemination. Therefore, it is necessary to conduct a comprehensive and objective assessment of large language models. This article will combine relevant data and analyze the main language models from both objective ability testing and subjective user experience aspects through some evaluation methods. In the collected articles, the respective data and limitations have all been explained. All have undergone in-depth research and reached a conclusion. These articles provide data and conclusion support for this paper, which can be summarized to draw new conclusions. The performance evaluation of large language models is a complex study. This article systematically reviews the main viewpoints and methodological analyses of this study. Each method has its own advantages and limitations. In the future, more comprehensive, authoritative and all-round evaluation methods should be studied. This can provide a framework reference for future research.