Benchmarking is seen as critical to assessing progress in NLP. However,
creating a benchmark involves many design decisions (e.g., which datasets to
include, which metrics to use) that often rely on tacit, untested assumptions
about what the benchmark is intended to measure or is actually measuring. There
is currently no principled way of analyzing these decisions and how they impact
the validity of the benchmark's measurements. To address this gap, we draw on
evidence-centered design in educational assessments and propose
Evidence-Centered Benchmark Design (ECBD), a framework which formalizes the
benchmark design process into five modules. ECBD specifies the role each module
plays in helping practitioners collect evidence about capabilities of interest.
Specifically, each module requires benchmark designers to describe, justify,
and support benchmark design choices -- e.g., clearly specifying the
capabilities the benchmark aims to measure or how evidence about those
capabilities is collected from model responses. To demonstrate the use of ECBD,
we conduct case studies with three benchmarks: BoolQ, SuperGLUE, and HELM. Our
analysis reveals common trends in benchmark design and documentation that could
threaten the validity of benchmarks' measurements.

综合证据为中心的基准设计（ECBD）框架确定角色，以帮助从案例研究中收集与能力相关的证据，同时发现了基准设计和文档编写的常见趋势可能会威胁基准测试的有效性。