BenchmarkCards: Standardized Documentation for Large Language Model Benchmarks
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Abstract (arXiv)
Large language models (LLMs) are powerful tools capable of handling diverse tasks. Comparing and selecting appropriate LLMs for specific tasks requires systematic evaluation methods, as models exhibit varying capabilities across different domains. However, finding suitable benchmarks is difficult given the many available options. This complexity not only increases the risk of benchmark misuse and misinterpretation but also demands substantial effort from LLM users, seeking the most suitable benchmarks for their specific needs. To address these issues, we introduce textttBenchmarkCards, an intuitive and validated documentation framework that standardizes critical benchmark attributes such as objectives, methodologies, data sources, and limitations. Through user studies involving benchmark creators and users, we show that textttBenchmarkCards can simplify benchmark selection and enhance transparency, facilitating informed decision-making in evaluating LLMs. Data & Code: https://github.com/SokolAnn/BenchmarkCards
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 34 of its 54 references that are in the map. First posted on arXiv 16 Oct 2024.
Builds on
34 of 54 references.
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22 more
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Built on by
Citing papers per year: 2025: 2; 2026: 6.
8 papers.
- 2025EvalCards: A Framework for Standardized Evaluation Reportingcited by 4
- 2026Every Eval Ever: A Unifying Schema and Community Repository for AI Evaluation Resultscited by 2
- 2025Beyond Benchmarks: LLM Evaluation with an Anthropomorphic and Lifecycle-oriented Roadmapcited by 1
- 2026Silicon Bureaucracy and AI Test-Oriented Education: Contamination Sensitivity and Score Confidence in LLM Benchmarkscited by 0
- 2026MMBench-Live: A Continuously Evolving Benchmark for Multimodal Modelscited by 0
- 2026ASSERT: A Measurement Pipeline for GenAI Auditscited by 0
- 2026When Evidence Changes the Subject: Subject-Typed Claim Licensing for Learned Routingcited by 0
- 2026Frozen Scenes, Shifting Winners: Configuration Fragility in Text-to-3D Evaluationcited by 0
- 2026Frozen Scenes, Shifting Winners: Configuration Fragility in Text-to-3D Evaluation30 Sep 2026
- 2026When Evidence Changes the Subject: Subject-Typed Claim Licensing for Learned Routing27 Sep 2026
- 2026ASSERT: A Measurement Pipeline for GenAI Audits14 Aug 2026
- 2026MMBench-Live: A Continuously Evolving Benchmark for Multimodal Models2 Jul 2026
- 2026Every Eval Ever: A Unifying Schema and Community Repository for AI Evaluation Results12 Jun 2026
- 2026Silicon Bureaucracy and AI Test-Oriented Education: Contamination Sensitivity and Score Confidence in LLM Benchmarks23 Mar 2026
- 2025EvalCards: A Framework for Standardized Evaluation Reporting5 Nov 2025
- 2025Beyond Benchmarks: LLM Evaluation with an Anthropomorphic and Lifecycle-oriented Roadmap26 Aug 2025