通过智能筛选提升评测集质量，让模型评估更高效准确。
Improving Model Evaluation using SMART Filtering of Benchmark Datasets
- 基于难易度、数据污染和相似性三重标准筛选高质量测试题
- 平均缩减48%数据量，同时提升与人工评估的相关性
- 适合希望优化评测流程的研究者和开发者
当前自然语言处理领域的评测面临基准饱和、数据污染及测试样本质量不均等挑战。为此，我们提出一种名为SMART的筛选方法，通过系统剔除低信息量和低挑战性的题目，从现有评测集中选取高质量子集。该方法依据三个标准：剔除简单题目、剔除受污染题目、剔除嵌入空间中相似的题目。我们在三个多项选择问答数据集上验证了SMART的有效性，结果显示其平均将数据集规模缩减48%，同时显著提升与ChatBot Arena（一个开放式人工评估设置）排名的皮尔逊相关性。该方法既可使新评测集更具挑战性，也能激活旧数据集，且保持模型间的相对排序不变。
原文摘要 · Abstract (English)
One of the most challenging problems facing NLP today is evaluation. Some of the most pressing issues pertain to benchmark saturation, data contamination, and diversity in the quality of test examples. To address these concerns, we propose Selection Methodology for Accurate, Reduced, and Targeted (SMART) filtering, a novel approach to select a high-quality subset of examples from existing benchmark datasets by systematically removing less informative and less challenging examples. Our approach applies three filtering criteria, removing (i) easy examples, (ii) data-contaminated examples, and (iii) examples that are similar to each other based on distance in an embedding space. We demonstrate the effectiveness of SMART on three multiple choice QA datasets, where our methodology increases efficiency by reducing dataset size by 48\% on average, while increasing Pearson correlation with rankings from ChatBot Arena, a more open-ended human evaluation setting. Our method enables us to be more efficient, whether using SMART to make new benchmarks more challenging or to revitalize older datasets, while still preserving the relative model rankings.
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