Summary of Betterbench: Assessing Ai Benchmarks, Uncovering Issues, and Establishing Best Practices, by Anka Reuel and Amelia Hardy and Chandler Smith and Max Lamparth and Malcolm Hardy and Mykel J. Kochenderfer
BetterBench: Assessing AI Benchmarks, Uncovering Issues, and Establishing Best Practices
by Anka Reuel, Amelia Hardy, Chandler Smith, Max Lamparth, Malcolm Hardy, Mykel J. Kochenderfer
First submitted to arxiv on: 20 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper evaluates the quality and usability of 24 AI benchmarks, considering best practices across their lifecycle. The authors develop an assessment framework that highlights significant issues with commonly used benchmarks, such as lack of statistical significance reporting or replicability. They identify large quality differences among benchmarks and provide a checklist for minimum quality assurance to support developers. Additionally, they create a living repository of benchmark assessments to facilitate comparability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AI models are being used in high-stakes environments, making it important to assess their capabilities and risks. Benchmarks help measure model performance and compare progress. But not all benchmarks are the same – their quality depends on design and usability. This paper looks at 24 AI benchmarks and finds that some have big problems. It creates a checklist for developers to follow and a place where people can compare benchmarks. |
