The swift advancement in large language models (LLMs) has heightened the importance of model evaluations. LLMs have acquired a substantial amount of knowledge, and evaluating the knowledge of these LLMs is crucial. To address this, we introduce the ZhuJiu-Knowledge benchmark which carefully considers the following factors: (1) For knowledge scope, we concentrate on three domains: commonsense knowledge, world knowledge, language knowledge, which comes from ATOMIC, Conceptnet, Wikidata, and Wordnet. (2) For data construction, to prevent data contamination, we utilize knowledge derived from corpora and knowledge graphs to formulate novel questions which are ensured not to appear in the training corpus. A multitude of prompts is purposefully devised to mitigate the impact of prompt design on evaluation and to further analyze the LLMs’ sensitivity to various prompts. (3) For evaluation criteria, we propose a novel voting methodology for assessing generative text, aligning the model’s evaluation with human preferences to reduce biases inherent in individual model assessments. We evaluate 14 current mainstream LLMs and conduct a comprehensive discussion and analysis of their results. The ZhuJiu-Knowledge benchmark and open-participation leaderboard are publicly released at http://zhujiu-knowledge.top and we also provide a demo video at https://youtu.be/QJp4qlEHVH8.

The unprecedented performance of LLMs requires comprehensive and accurate evaluation. We argue that for LLMs evaluation, benchmarks need to be comprehensive and systematic. To this end, we propose the Zhujiu benchmark, which has the following strengths: (1) Multi-dimensional ability coverage: We comprehensively evaluate LLMs across 7 ability dimensions covering 51 tasks. Especially, we also propose a new benchmark that focus on knowledge ability of LLMs. (2) Multi-faceted evaluation methods collaboration: We use 3 different yet complementary evaluation methods to comprehensively evaluate LLMs, which can ensure the authority and accuracy of the evaluation results. (3) Comprehensive Chinese benchmark: ZhuJiu is the pioneering benchmark that fully assesses LLMs in Chinese, while also providing equally robust evaluation abilities in English. (4) Avoiding potential data leakage: To avoid data leakage, we construct evaluation data specifically for 37 tasks. We evaluate 10 current mainstream LLMs, and conduct an in-depth discussion and analysis of their results. The ZhuJiu benchmark and open-participation leaderboard are publicly released at http://www.zhujiu-benchmark.com and we also provide a demo video at https://youtu.be/qypkJ89L1Ic.