---
id: 20260524-T0-05
title: "新方法解决大模型评估数据污染问题"
title_en: "New Method Tackles LLM Benchmark Data Contamination"
url: https://ai.daily.yangsir.net/daily/20260524-T0-05
issue_date: 2026-05-24
publish_date: 2026-05-23T04:00:00.000Z
source_name: "arXiv cs.LG (ML)"
source_url: https://arxiv.org/abs/2605.21543
---

# 新方法解决大模型评估数据污染问题

研究人员提出可证明的联合去污染方法，解决大模型评估中的数据污染问题。当评估数据出现在被评估模型的训练集中时，会严重影响性能评估的准确性。该方法能够在多个模型之间准确识别和去除污染数据，确保评估结果的可靠性。

## English Version

**New Method Tackles LLM Benchmark Data Contamination**

Researchers develop provable joint decontamination method to address LLM benchmark data contamination. The approach accurately identifies and removes contaminated evaluation data from training sets, ensuring reliable cross-model performance comparisons.

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**来源**：[arXiv cs.LG (ML)](https://arxiv.org/abs/2605.21543)

**详情页**：https://ai.daily.yangsir.net/daily/20260524-T0-05

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