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About this paper
Michael Saxon, Ari Holtzman, Peter West, William Yang Wang, Naomi Saphra
This paper advocates for establishing "model metrology" as an independent scientific discipline to address the inadequacy of current large language model (LLM) benchmarks, proposing the development of constrained, dynamic, and plug-and-play evaluation methods to better assess real-world utility and deployment tolerances. It identifies fundamental flaws in existing evaluation practices, such as benchmark saturation and a lack of construct validity, and outlines a roadmap for professionalizing AI measurement.
Key insights
- Current AI benchmarks often lack both "construct validity" (accurately measuring abstract capabilities) and "ecological validity" (predicting real-world performance).
- The continued reliance on flawed benchmarking practices is exacerbated by misaligned incentives among AI researchers, model developers, and end-users.
- A dedicated, independent discipline akin to classical metrology is necessary to provide rigorous, reliable measurement of AI system capabilities.
Summary by alphaXiv.
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