大模型
arxiv.org ·
别信基准：通用 LLM 排名的局限与任务特定评估的必要性
arXiv 观点论文指出通用 LLM 排行榜存在五类相互关联的局限：被评测系统与公开可用系统不一致、外部评测中的商业激励与依赖、基准饱和与缺陷测试及数据污染、模型利用评分流程、通用分数与用户实际任务相关性有限。作者主张评测应披露测试配置、验证题目与任务完成情况、同时报告性能与成本及执行时间，并明确泛化范围；文中以众包评测平台 Isotanta 作为贡献题目与重复评估的实践示例，但指出更大的题库与重复采样均不保证效度或个性化，核心论点是模型选择需要针对目标工作的证据而非排行榜名次。
AI推荐理由 arXiv 观点论文，系统梳理通用 LLM 排行榜的五类局限（评测系统与公开系统不一致、外部评测的商业激励、基准饱和与数据污染、模型针对评分流程优化、通用分数与用户任务相关性有限），并提出披露测试配置、验证题目与任务完成、同时报告成本与执行时间、明确泛化范围等评估建议，还以众包评测平台 Isotanta 为例讨论贡献题目与重复采样。属于方法论层面的批判性讨论，对模型选型与评测实践有参考价值；但为观点性论文，未提供新的实验数据或可验证结果，Isotanta 部分也仅作示例，未证明其有效性。
72
正文
Computer Science > Artificial Intelligence
arXiv:2609.23201 (cs)
[Submitted on 19 Sep 2026]
Title:Do Not Trust the Benchmark: Limitations of General LLM Rankings and a Case for Task-Specific Evaluation
Authors:Danial Amin
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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.
Subjects:
Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
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arXiv:2609.23201 [cs.AI]
(or
arXiv:2609.23201v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2609.23201
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arXiv-issued DOI via DataCite (pending registration)
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From: Danial Amin [view email]
[v1]
Sat, 19 Sep 2026 20:02:05 UTC (335 KB)
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