摘要：基准测试塑造了模型能力被衡量和解读的方式，但其分数可能掩盖了哪些群体的惯例、语境和判断定义了何为成功表现。我们提出了一套以用户为中心的审计框架，区分了为基准贡献内容的用户，以及评测结果意图代表其需求的用户。该框架围绕基准内容、元数据、文档和评分程序，对七个维度进行操作化审计。对 16 项资源的分析显示，在 11 个文本基准中，以美国为中心的惯例占匹配信号的 62.4% 至 94.4%；而在 5 项图文资源中，地理可见性高度集中，并且与国家 GDP 占比存在强相关性（平均 Pearson r = 0.780；Spearman p = 0.778）。文档分析揭示出系统性的来源—目标差距：全部 16 项资源都对来源用户语境进行了操作化处理，但没有任何一项对目标用户语境进行操作化处理。配对评分实验进一步表明，语义保持不变的答案变体会使 DROP 的精确匹配准确率从 100.0% 降至 33.3%；而 TruthfulQA 则表现出取决于度量指标的参考答案相似度下降。我们的框架和文档检查清单使用户假设能够被审计，从而明确基准分数所代表的含义，以及评测主张在哪些方面获得支持。

Abstract:Benchmarks shape how model capabilities are measured and interpreted, yet their scores can obscure whose conventions, contexts, and judgments define successful performance. We introduce a user-centric audit framework that distinguishes users who contribute benchmark content from those whose needs evaluation results are intended to represent. The framework operationalizes seven dimensions across benchmark content, metadata, documentation, and scoring procedures. Across 16 resources, we find that American-centered conventions account for 62.4-94.4% of matched signals in 11 text benchmarks, while geographic visibility in five image-text resources is concentrated and strongly associated with national GDP share (mean Pearson r = 0.780; Spearman p = 0.778). Documentation reveals a systematic source-target gap: all 16 resources operationalize source-user context, but none operationalize target-user context. Paired scoring experiments further show that meaning-preserving answer variants reduce DROP exact-match accuracy from 100.0% to 33.3%, while TruthfulQA exhibits metric-dependent reductions in reference similarity. Our framework and documentation checklist make user assumptions auditable, clarifying what benchmark scores represent and where evaluation claims are supported.