E-EVAL: A Comprehensive Chinese K-12 Education Evaluation Benchmark for Large Language Models
This addresses the need for accurate assessment of LLMs in Chinese K-12 education, which is crucial for integrating AI into educational applications, though it is incremental as it builds on existing benchmarking efforts by focusing on a specific domain.
The paper tackles the lack of a benchmark for evaluating large language models (LLMs) in the Chinese K-12 education domain by introducing E-EVAL, a comprehensive benchmark with 4,351 multiple-choice questions across various subjects, finding that Chinese-dominant models often outperform English-dominant ones, with many scoring above GPT-4.0, but all models struggle in complex subjects like mathematics.
With the accelerating development of Large Language Models (LLMs), many LLMs are beginning to be used in the Chinese K-12 education domain. The integration of LLMs and education is getting closer and closer, however, there is currently no benchmark for evaluating LLMs that focuses on the Chinese K-12 education domain. Therefore, there is an urgent need for a comprehensive natural language processing benchmark to accurately assess the capabilities of various LLMs in the Chinese K-12 education domain. To address this, we introduce the E-EVAL, the first comprehensive evaluation benchmark specifically designed for the Chinese K-12 education field. The E-EVAL consists of 4,351 multiple-choice questions at the primary, middle, and high school levels across a wide range of subjects, including Chinese, English, Politics, History, Ethics, Physics, Chemistry, Mathematics, and Geography. We conducted a comprehensive evaluation of E-EVAL on advanced LLMs, including both English-dominant and Chinese-dominant models. Findings show that Chinese-dominant models perform well compared to English-dominant models, with many scoring even above the GPT 4.0. However, almost all models perform poorly in complex subjects such as mathematics. We also found that most Chinese-dominant LLMs did not achieve higher scores at the primary school level compared to the middle school level. We observe that the mastery of higher-order knowledge by the model does not necessarily imply the mastery of lower-order knowledge as well. Additionally, the experimental results indicate that the Chain of Thought (CoT) technique is effective only for the challenging science subjects, while Few-shot prompting is more beneficial for liberal arts subjects. With E-EVAL, we aim to analyze the strengths and limitations of LLMs in educational applications, and to contribute to the progress and development of Chinese K-12 education and LLMs.