English(EN) A Unified Perturbation Framework for Analyzing Leaderboard Stability and Manipulation

新框架揭示LLM排行榜易受操纵

作者 PulseAugur 编辑部 · [1 个来源] · 2026-05-15 09:21

研究人员开发了一个统一的框架，用于分析大型语言模型评估排行榜的稳定性和潜在操纵性。他们的研究使用了Chatbot Arena等数据集，揭示了当前的排行榜极易受到微小数据扰动的影响，这会改变排名靠前的模型和置信区间。该框架不仅审计这些漏洞，还提供了高效定向操纵的方法，凸显了对更鲁棒的评估协议的需求。 AI

影响凸显了LLM评估中的漏洞，可能导致更可靠的基准测试和更公平的模型比较。

排序理由该集群包含一篇学术论文，详细介绍了用于分析LLM排行榜的新框架。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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arXiv cs.LG TIER_1 English(EN) · Amir-Hossein Karimi · 2026-05-15 09:21

用于分析排行榜稳定性和操纵的统一扰动框架

Evaluation leaderboards such as LMArena play a central role in benchmarking large language models by aggregating pairwise human preferences into model rankings, yet the robustness of these rankings remains poorly understood. We present a unified perturbation framework for analyzi…

本文如何被排名

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用于分析排行榜稳定性和操纵的统一扰动框架

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