Summary of Foundabench: Evaluating Chinese Fundamental Knowledge Capabilities Of Large Language Models, by Wei Li et al.
FoundaBench: Evaluating Chinese Fundamental Knowledge Capabilities of Large Language Models
by Wei Li, Ren Ma, Jiang Wu, Chenya Gu, Jiahui Peng, Jinyang Len, Songyang Zhang, Hang Yan, Dahua Lin, Conghui He
First submitted to arxiv on: 29 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces FoundaBench, a novel benchmark designed to comprehensively assess the fundamental knowledge capabilities of Chinese large language models (LLMs). The benchmark comprises 3354 multiple-choice questions across common sense and K-12 educational subjects, carefully curated to reflect everyday and academic knowledge. Twelve state-of-the-art LLMs are evaluated using FoundaBench, employing traditional assessment methods and a circular evaluation protocol to mitigate potential biases. Results show that models pre-trained on Chinese corpora outperform others, highlighting a significant disparity between reasoning and memory recall capabilities. The study sets a new standard for understanding the fundamental knowledge of LLMs, providing a robust framework for future advancements. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a special test for big language models to see how well they know basic facts. It’s focused on Chinese language and culture. They made 3354 questions about common sense and school subjects to measure how good these models are. They tested 12 top models using this new way of testing, which helps remove any biases. The results show that models trained on lots of Chinese text do better than others. This study helps us understand what language models know and will help make them even better. |
Keywords
» Artificial intelligence » Recall
