Unmasking the Flaws: Why Current LLM Benchmarks Fail to Measure True AI Capability
By Andrew M. Bean, Ryan Othniel Kearns, Angelika Romanou, Franziska Sofia Hafner, Harry Mayne, Jan Batzner, Negar Foroutan, Chris Schmitz, Karolina Korgul, Hunar Batra, Oishi Deb, Emma Beharry, Cornelius Emde, Thomas Foster, Anna Gausen, Mar\'ia Grandury, Simeng Han, Valentin Hofmann, Lujain Ibrahim, Hazel Kim, Hannah Rose Kirk, Fangru Lin, Gabrielle Kaili-May Liu, Lennart Luettgau, Jabez Magomere, Jonathan Rystr{\o}m, Anna Sotnikova, Yushi Yang, Yilun Zhao, Adel Bibi, Antoine Bosselut, Ronald Clark, Arman Cohan, Jakob Foerster, Yarin Gal, Scott A. Hale, Inioluwa Deborah Raji, Christopher Summerfield, Philip H. S. Torr, Cozmin Ududec, Luc Rocher, Adam Mahdi
Published on November 10, 2025| Vol. 1, Issue No. 1
Content Source
This is a curated briefing. The original article was published on cs.CL updates on arXiv.org.
Summary
Evaluating Large Language Models (LLMs) for complex attributes like 'safety' and 'robustness' is critical for responsible deployment. However, a systematic review of 445 LLM benchmarks across leading NLP/ML conferences, conducted by 29 experts, reveals significant issues with 'construct validity.' This means many benchmarks fail to truly measure the intended phenomena due to shortcomings in their design, tasks, and scoring metrics. The paper offers eight key recommendations and actionable guidance to improve future LLM benchmark development for researchers and practitioners.
Why It Matters
This research highlights a fundamental crisis in how we assess and understand the true capabilities and risks of Large Language Models. If the very benchmarks we rely on to judge LLM performance, safety, and robustness are flawed-lacking 'construct validity'-then much of the progress we claim, and the trust we place in these models, could be built on shaky ground. For AI professionals, this isn't merely an academic concern; it has profound practical implications. Developers risk deploying models with unverified safety or robustness claims, potentially leading to real-world failures, ethical dilemmas, or even regulatory penalties. Researchers are challenged to move beyond the pursuit of leaderboard dominance with superficial metrics, pushing instead for more rigorous, scientifically sound evaluation methodologies. Furthermore, for policymakers and industry leaders, invalid benchmarks undermine the ability to make informed decisions about AI regulation, investment, and responsible integration. The paper underscores a critical need for the AI community to mature its measurement practices, perhaps by drawing lessons from disciplines with long-standing expertise in assessing complex, abstract concepts. Ultimately, understanding and addressing these benchmark limitations is paramount for fostering genuinely capable, trustworthy, and safe AI systems that can deliver on their immense promise without unforeseen societal costs.