FoundaBench: Evaluating Chinese Fundamental Knowledge Capabilities of Large Language Models

• URL: http://arxiv.org/abs/2404.18359v1
• Date: Mon, 29 Apr 2024 01:49:07 GMT
• Title: FoundaBench: Evaluating Chinese Fundamental Knowledge Capabilities of Large Language Models
• Authors: Wei Li, Ren Ma, Jiang Wu, Chenya Gu, Jiahui Peng, Jinyang Len, Songyang Zhang, Hang Yan, Dahua Lin, Conghui He,
• Abstract summary: This paper introduces FoundaBench, a pioneering benchmark designed to rigorously evaluate the fundamental knowledge capabilities of Chinese LLMs. 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.
• Score: 64.11333762954283
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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