# ECBD: Evidence-Centered Benchmark Design for NLP

arXiv: https://arxiv.org/abs/2406.08723 · HTML: https://mlsanity.com/paper/2406.08723/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 34 references that are in the map.
- First posted on arXiv 13 Jun 2024.

## Builds on (most cited first, 19 of 19)

- [GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding](https://mlsanity.com/paper/1804.07461/) (2018)
- [BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions](https://mlsanity.com/paper/1905.10044/) (2019)
- [SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems](https://mlsanity.com/paper/1905.00537/) (2019)
- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [Language (Technology) is Power: A Critical Survey of "Bias" in NLP](https://mlsanity.com/paper/2005.14050/) (2020)
- [Quantifying Language Models' Sensitivity to Spurious Features in Prompt Design or: How I learned to start worrying about prompt formatting](https://mlsanity.com/paper/2310.11324/) (2023)
- [CrowS-Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language Models](https://mlsanity.com/paper/2010.00133/) (2020)
- [Annotation Artifacts in Natural Language Inference Data](https://mlsanity.com/paper/1803.02324/) (2018)
- [Dynabench: Rethinking Benchmarking in NLP](https://mlsanity.com/paper/2104.14337/) (2021)
- [Measurement and Fairness](https://mlsanity.com/paper/1912.05511/) (2019)
- [What Will it Take to Fix Benchmarking in Natural Language Understanding?](https://mlsanity.com/paper/2104.02145/) (2021)
- [When Benchmarks are Targets: Revealing the Sensitivity of Large Language Model Leaderboards](https://mlsanity.com/paper/2402.01781/) (2024)
- [Rethinking Model Evaluation as Narrowing the Socio-Technical Gap](https://mlsanity.com/paper/2306.03100/) (2023)
- [Just What do You Think You're Doing, Dave?' A Checklist for Responsible Data Use in NLP](https://mlsanity.com/paper/2109.06598/) (2021)
- [Connecting Algorithmic Research and Usage Contexts: A Perspective of Contextualized Evaluation for Explainable AI](https://mlsanity.com/paper/2206.10847/) (2022)
- [How Robust are Model Rankings: A Leaderboard Customization Approach for Equitable Evaluation](https://mlsanity.com/paper/2106.05532/) (2021)
- [Challenges in Measuring Bias via Open-Ended Language Generation](https://mlsanity.com/paper/2205.11601/) (2022)
- [Machine Learning Data Practices through a Data Curation Lens: An Evaluation Framework](https://mlsanity.com/paper/2405.02703/) (2024)

## Built on by (most cited first, 28 of 28)

- [BetterBench: Assessing AI Benchmarks, Uncovering Issues, and Establishing Best Practices](https://mlsanity.com/paper/2411.12990/) (2024)
- [TRAIL: Trace Reasoning and Agentic Issue Localization](https://mlsanity.com/paper/2505.08638/) (2025)
- [Benchmarks as Microscopes: A Call for Model Metrology](https://mlsanity.com/paper/2407.16711/) (2024)
- [When AI Benchmarks Plateau: A Systematic Study of Benchmark Saturation](https://mlsanity.com/paper/2602.16763/) (2026)
- [Eureka: Evaluating and Understanding Large Foundation Models](https://mlsanity.com/paper/2409.10566/) (2024)
- [TALES: Text Adventure Learning Environment Suite](https://mlsanity.com/paper/2504.14128/) (2025)
- [Can LLMs Reason Abstractly Over Math Word Problems Without CoT? Disentangling Abstract Formulation From Arithmetic Computation](https://mlsanity.com/paper/2505.23701/) (2025)
- [Browsing Lost Unformed Recollections: A Benchmark for Tip-of-the-Tongue Search and Reasoning](https://mlsanity.com/paper/2503.19193/) (2025)
- [Rigor in AI: Doing Rigorous AI Work Requires a Broader, Responsible AI-Informed Conception of Rigor](https://mlsanity.com/paper/2506.14652/) (2025)
- [Learning Task-Agnostic Representations through Multi-Teacher Distillation](https://mlsanity.com/paper/2510.18680/) (2025)
- [EvalCards: A Framework for Standardized Evaluation Reporting](https://mlsanity.com/paper/2511.21695/) (2025)
- [AI Evaluation Should Require Standardized Item-Level Data Releases](https://mlsanity.com/paper/2604.03244/) (2026)
- [pdfQA: Diverse, Challenging, and Realistic Question Answering over PDFs](https://mlsanity.com/paper/2601.02285/) (2026)
- [Measurement as Bricolage: Examining How Data Scientists Construct Target Variables for Predictive Modeling Tasks](https://mlsanity.com/paper/2507.02819/) (2025)
- [BenchBrowser: Retrieving Evidence for Evaluating Benchmark Validity](https://mlsanity.com/paper/2603.18019/) (2026)
- [Measurement Without Validity: The Compounding Reliability Problem in Agentic AI Evaluation](https://mlsanity.com/paper/2608.00794/) (2026)
- [Do Assessment Instruments Measure the Same Thing for Humans and LLMs? A Latent Structure Analysis](https://mlsanity.com/paper/2608.15630/) (2026)
- [Harbor Adapters and Harbor-Index: Infrastructure and a Curated Meta-Dataset for Large-Scale Agentic Evaluation](https://mlsanity.com/paper/2609.04298/) (2026)
- [A Novel Psychometrics-Based Approach to Developing Professional Competency Benchmark for Large Language Models](https://mlsanity.com/paper/2411.00045/) (2024)
- [Towards Real-World Validity in Generative AI Benchmarks: Understanding and Designing Domain-Centered Evaluations for Journalism Practitioners](https://mlsanity.com/paper/2511.05501/) (2025)
- [Results-Actionability Gap: Understanding How Practitioners Evaluate LLM Products in the Wild](https://mlsanity.com/paper/2604.16304/) (2026)
- [When benchmark inferences do not compose: Projectibility in AI evaluation](https://mlsanity.com/paper/2607.26159/) (2026)
- [The Benchmark Trap: Structures of Power and Injustice in AI Evaluations](https://mlsanity.com/paper/2608.15326/) (2026)
- [Interpretable Humans, Alien LLMs: Expert Analysis of Latent Structures in Assessment Responses](https://mlsanity.com/paper/2608.17810/) (2026)
- [BENCHCOMPASS: From Scores to Signals for Training and Harness Decisions in Payment-Domain LLMs](https://mlsanity.com/paper/2609.18270/) (2026)
- [LLJ Cards: Best practices for the Use of LLMs as Judges](https://mlsanity.com/paper/2609.24516/) (2026)
- [From annotation to reasoning: Culture in language models](https://mlsanity.com/paper/2609.30897/) (2026)
- [Whose Data, Whose Benchmark? A User-Centric Framework for Auditing Evaluation Resources](https://mlsanity.com/paper/2610.19268/) (2026)

## Abstract (arXiv)

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.