Measuring what Matters: Construct Validity in Large Language Model Benchmarks
Published 3 Nov 2025 · arXiv · Andrew M. Bean
Overview
This paper examines the construct validity of benchmarks used to evaluate large language models (LLMs). It identifies significant issues in current benchmarks that undermine the validity of claims about LLM capabilities, particularly concerning safety and robustness.
Key Insights
- Construct Validity Issues: Many benchmarks fail to accurately measure abstract phenomena like safety and robustness.
- Systematic Review: The paper reviews 445 benchmarks from leading conferences, revealing patterns that compromise validity.
- Recommendations: Eight key recommendations are provided to improve benchmark development.
BFSI Relevance
- Why Relevant: Reliable evaluation of LLMs is crucial for their deployment in BFSI sectors, where safety and robustness are paramount.
- Primary Sector: Financial Services
- Subsectors: Risk Management, Compliance
- Actionable Implications:
- BFSI professionals should advocate for benchmarks with strong construct validity.
- Incorporate robust LLM evaluations in risk management strategies.
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