Limitations of general large language model rankings for users
Do Not Trust the Benchmark: Limitations of General LLM Rankings and a Case for Task-Specific Evaluation
Artificial IntelligenceHuman-Computer Interaction
Summary
Benchmark scores for large language models (LLMs) are often used to compare and choose models, but these scores can be misleading. The authors show that differences in evaluation setups, commercial influences, flawed test questions, and models gaming the scoring can distort general rankings. They argue that choosing an LLM should be based on how well it performs on specific tasks users actually need. The paper also describes Isotanta, a platform that collects many questions and repeatedly tests models to improve evaluation stability and task relevance.
What this means in practice
- •For software developers: Identify appropriate language models by testing performance on your specific application tasks rather than relying on general leaderboards.
- •For product managers: Improve model selection decisions by considering cost, execution time, and task relevance instead of only benchmark scores.
A position paper. It proposes an approach and reports no results.
Authors
Danial Amin
Abstract
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 users' tasks. Documented cases illustrate why these problems require different responses. I argue for evaluation procedures that disclose the tested configuration, validate questions and successful task completion, report performance alongside cost and execution time, and make the scope of generalization explicit. I then discuss \textbf{Isotanta}, a crowdsourced benchmarking platform, as a practical example of contributed questions and repeated evaluation. A larger question pool may improve task coverage, while repeated sampling can improve the stability of estimates on that pool; neither guarantees validity or personalization. The paper distinguishes the platform's current shared ranking from proposed task-specific and user-provided evaluations. Its central argument is that model selection requires evidence about performance on the intended work, not simply a high position on a general leaderboard.