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Recently, various Large Language Models (LLMs) evaluation datasets haveemerged, but most of them have issues with distorted rankings and difficulty inmodel capabilities analysis. Addressing these concerns, this paper introducesANGO, a Chinese multi-choice question evaluation benchmark. ANGO proposes\textit{Keypoint} categorization standard for the first time, each question inANGO can correspond to multiple keypoints, effectively enhancinginterpretability of evaluation results. Base on performance of real humans, webuild a quantifiable question difficulty standard and divide ANGO questionsinto 9 difficulty levels, which provide more precise guidance for modeltraining. To minimize data leakage impact and fully leverage ANGO's innovativefeatures, we have engineered exclusive sampling strategies and a new evaluationframework that support swift testset iteration. Our experiments demonstratethat ANGO poses a stronger challenge to models and reveals more details inevaluation result compared to existing benchmarks.

ANGO: A Next-Level Evaluation Benchmark For Generation-Oriented Language Models In Chinese Domain