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Do Not Trust the Benchmark: Limitations of General LLM Rankings and a Case for Task-Specific Evaluation

arXiv · AI, language, vision and robotics · article · Sep 19, 2026 · UTC

Benchmark scores increasingly influence the development, marketing, and selection of large language models (LLMs). Yet an overall score is interpretable only in relation to the system tested, the questions included, and the conditions of evaluation. This perspective examines five connected limitations of general LLM rankings: differences between evaluated and publicly available systems; commercial incentives and dependencies in external evaluation; benchmark saturation, defective tests, and data contamination; models exploiting scoring procedures; and the limited relevance of general scores to

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