FoundaBench: Evaluating Chinese Fundamental Knowledge Capabilities of Large Language Models
<|reference_start|>FoundaBench: Evaluating Chinese Fundamental Knowledge Capabilities of Large Language Models: In the burgeoning field of large language models (LLMs), the assessment of fundamental knowledge remains a critical challenge, particularly for models tailored to Chinese language and culture. This paper introduces FoundaBench, a pioneering benchmark designed to rigorously evaluate the fundamental knowledge capabilities of Chinese LLMs. FoundaBench encompasses a diverse array of 3354 multiple-choice questions across common sense and K-12 educational subjects, meticulously curated to reflect the breadth and depth of everyday and academic knowledge. We present an extensive evaluation of 12 state-of-the-art LLMs using FoundaBench, employing both traditional assessment methods and our CircularEval protocol to mitigate potential biases in model responses. Our results highlight the superior performance of models pre-trained on Chinese corpora, and reveal a significant disparity between models' reasoning and memory recall capabilities. The insights gleaned from FoundaBench evaluations set a new standard for understanding the fundamental knowledge of LLMs, providing a robust framework for future advancements in the field.<|reference_end|>
@article{li2024foundabench:,
    title={FoundaBench: Evaluating Chinese Fundamental Knowledge Capabilities of
  Large Language Models},
    author={Wei Li, Ren Ma, Jiang Wu, Chenya Gu, Jiahui Peng, Jinyang Len,
  Songyang Zhang, Hang Yan, Dahua Lin, Conghui He},
    journal={arXiv preprint arXiv:2404.18359},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.18359},
    primaryClass={cs.CL cs.AI}
}