  Recently, various Large Language Models (LLMs) evaluation datasets have
emerged, but most of them have issues with distorted rankings and difficulty in
model capabilities analysis. Addressing these concerns, this paper introduces
ANGO, a Chinese multi-choice question evaluation benchmark. ANGO proposes
Keypoint categorization standard for the first time, each question in ANGO can
correspond to multiple keypoints, effectively enhancing interpretability of
evaluation results. Base on performance of real humans, we build a quantifiable
question difficulty standard and divide ANGO questions into 9 difficulty
levels, which provide more precise guidance for model training. To minimize
data leakage impact and fully leverage ANGO's innovative features, we have
engineered exclusive sampling strategies and a new evaluation framework that
support swift testset iteration. Our experiments demonstrate that ANGO poses a
stronger challenge to models and reveals more details in evaluation result
compared to existing benchmarks.
