# C-Eval

C-Eval is a comprehensive Chinese evaluation benchmark for assessing large language models across 52 subjects, covering basic to advanced education levels, with 13,948 multiple-choice questions.

C-Eval is a comprehensive evaluation benchmark designed to assess the capabilities of large language models (LLMs) on a wide range of knowledge and reasoning tasks in Chinese. Developed by researchers at Tsinghua University and other institutions, it was introduced in 2023 to address the need for a robust, Chinese-language benchmark that could measure the performance of LLMs in a manner comparable to English-centric benchmarks like MMLU. The benchmark comprises 13,948 multiple-choice questions spanning 52 distinct subjects, ranging from middle school to professional and graduate levels, and is widely used to gauge the general knowledge and reasoning abilities of models in the Chinese language context.

The creation of C-Eval was motivated by the observation that most existing evaluation benchmarks were predominantly in English, which limited their applicability to models trained or deployed in other languages. As large language models like those from [openai](https://www.wikiprompt.org/wiki/openai), [anthropic](https://www.wikiprompt.org/wiki/anthropic), and [google-deepmind](https://www.wikiprompt.org/wiki/google-deepmind) began to show impressive capabilities, the need for culturally and linguistically appropriate evaluation tools became evident. C-Eval fills this gap by providing a standardized test that covers a broad spectrum of disciplines, including humanities, social sciences, STEM fields, and more, all presented in Chinese. Its design allows for the assessment of both factual recall and multi-step reasoning, making it a valuable tool for researchers and developers in the field of [artificial-intelligence](https://www.wikiprompt.org/wiki/artificial-intelligence).

## Structure and Content

C-Eval is organized into four difficulty levels: middle school, high school, college, and professional. The questions are sourced from standardized Chinese exams and professional certification tests, ensuring a high level of quality and relevance. The 52 subjects include areas such as mathematics, physics, chemistry, biology, history, geography, law, medicine, and computer science, among others. Each question is a multiple-choice format with four options, and the benchmark provides both a zero-shot and a few-shot evaluation setting, allowing for flexible testing of model performance.

The benchmark's construction involved careful curation to avoid contamination with training data, a common issue in LLM evaluation. The questions were collected from publicly available exam materials and then filtered to ensure they were not present in common training corpora. This attention to data hygiene makes C-Eval a reliable indicator of a model's true generalization ability rather than mere memorization. The benchmark also includes a validation set and a test set, with the test set answers held out to prevent overfitting.

## Evaluation Methodology

To evaluate a model on C-Eval, researchers typically use a few-shot prompting approach, where the model is provided with a few examples from the validation set before being asked to answer questions from the test set. The model's performance is measured by its accuracy in selecting the correct answer. The benchmark supports both generation-based and perplexity-based evaluation methods, though the former is more common. In generation-based evaluation, the model is prompted to output the answer directly, and the response is compared against the ground truth.

C-Eval has become a standard benchmark in the Chinese AI community, with many model developers reporting their scores on it. For instance, models like [alibaba-cloud](https://www.wikiprompt.org/wiki/alibaba-cloud)'s Qwen series and other Chinese LLMs have been evaluated on C-Eval, and the results are often cited in technical reports and research papers. The benchmark's popularity stems from its comprehensive coverage and the fact that it provides a clear, quantitative measure of a model's knowledge in Chinese, which is crucial for applications targeting Chinese-speaking users.

## Significance and Impact

The introduction of C-Eval has had a significant impact on the development and evaluation of LLMs, particularly those aimed at the Chinese market. It has facilitated comparisons between models from different organizations, such as [baidu](https://www.wikiprompt.org/wiki/baidu) and [tencent](https://www.wikiprompt.org/wiki/tencent), and has driven improvements in model training and fine-tuning. By highlighting areas where models underperform, C-Eval helps guide research efforts toward addressing specific knowledge gaps or reasoning deficiencies.

Moreover, C-Eval has contributed to the broader discourse on AI evaluation, underscoring the importance of multilingual benchmarks. It has inspired the creation of similar benchmarks in other languages and has been used as a reference point for developing more culturally aware evaluation tools. The benchmark is openly available, and its leaderboard is maintained publicly, allowing researchers and practitioners to track the state of the art in Chinese-language AI.

## Limitations and Criticisms

Despite its widespread use, C-Eval is not without limitations. Critics have pointed out that the benchmark primarily tests factual knowledge and basic reasoning, which may not fully capture the nuanced capabilities of advanced LLMs, such as creativity, common-sense reasoning, or the ability to follow complex instructions. Additionally, the multiple-choice format can sometimes be gamed by models that exploit statistical patterns in the options, although the benchmark's design mitigates this to some extent.

Another concern is the potential for data contamination, as new models may be trained on data that includes C-Eval questions, leading to inflated scores. To address this, the benchmark maintainers periodically update the test set, but the risk remains. Furthermore, C-Eval's focus on Chinese may limit its applicability to models that are primarily English-centric, although it serves as a valuable complement to English benchmarks like MMLU.

## Future Directions

As the field of [large-language-model](https://www.wikiprompt.org/wiki/large-language-model)s continues to evolve, benchmarks like C-Eval will need to adapt to keep pace. Future versions may incorporate more dynamic question generation, include open-ended tasks, or expand to cover additional languages and domains. The success of C-Eval has demonstrated the value of domain-specific evaluation, and it is likely that similar benchmarks will emerge for other languages and specialized fields. For now, C-Eval remains a cornerstone in the evaluation of Chinese-language AI models, providing a rigorous and widely accepted measure of their capabilities.

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Source: https://www.wikiprompt.org/wiki/c-eval
License: CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0/)
Last updated: 2026-09-13T03:54:19.305783+00:00