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cs.CL2026
Mitigating Selection Bias in Large Language Models via Permutation-Aware GRPO
Jinquan Zheng, Jia Yuan, Jiacheng Yao +3
Large language models (LLMs) used for multiple-choice and pairwise evaluation tasks often exhibit selection bias due to non-semantic factors like option positions and label symbols…
cs.CL2024
Benchmarking Chinese Commonsense Reasoning of LLMs: From Chinese-Specifics to Reasoning-Memorization Correlations
Jiaxing Sun, Weiquan Huang, Jiang Wu +5
We introduce CHARM, the first benchmark for comprehensively and in-depth evaluating the commonsense reasoning ability of large language models (LLMs) in Chinese, which covers both…
cs.CL2024
FoundaBench: Evaluating Chinese Fundamental Knowledge Capabilities of Large Language Models
Wei Li, Ren Ma, Jiang Wu +7
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…