MEQA: A Meta-Evaluation Framework for Question & Answer LLM Benchmarks

• URL: http://arxiv.org/abs/2504.14039v1
• Date: Fri, 18 Apr 2025 19:01:53 GMT
• Title: MEQA: A Meta-Evaluation Framework for Question & Answer LLM Benchmarks
• Authors: Jaime Raldua Veuthey, Zainab Ali Majid, Suhas Hariharan, Jacob Haimes,
• Abstract summary: We propose MEQA, a framework for the meta-evaluation of question and answer (QA) benchmarks.<n>We demonstrate this approach on cybersecurity benchmarks, using human and LLM evaluators.<n>We motivate our choice of test domain by AI models' dual nature as powerful defensive tools and security threats.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: As Large Language Models (LLMs) advance, their potential for widespread societal impact grows simultaneously. Hence, rigorous LLM evaluations are both a technical necessity and social imperative. While numerous evaluation benchmarks have been developed, there remains a critical gap in meta-evaluation: effectively assessing benchmarks' quality. We propose MEQA, a framework for the meta-evaluation of question and answer (QA) benchmarks, to provide standardized assessments, quantifiable scores, and enable meaningful intra-benchmark comparisons. We demonstrate this approach on cybersecurity benchmarks, using human and LLM evaluators, highlighting the benchmarks' strengths and weaknesses. We motivate our choice of test domain by AI models' dual nature as powerful defensive tools and security threats.

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