提出基于不确定性的大模型去偏与遗忘新方法，有效解决数据污染问题。
arXiv:2606.23313v1 Announce Type: cross Abstract: Benchmark-based evaluation is the dominant paradigm for assessing large language model (LLM) capabil…
揭示深度研究代理中搜索时间污染导致基准性能虚高，为AI评估体系敲响警钟。
arXiv:2606.05241v1 Announce Type: cross Abstract: Public benchmarks enable fair and reproducible evaluation of LLM reasoning, but they become fragile …
逐层表示分析新方法，精准识别RL后训练阶段的数据污染，提升模型安全与可靠性。
arXiv:2605.29888v1 Announce Type: cross Abstract: Reinforcement learning (RL) post-training has shown to improve reasoning in large language models (L…
教你用数据投毒对抗AI：让模型失效的实战策略与工具解析
Article URL: https://www.youtube.com/watch?v=Z8aLGHmnRyc Comments URL: https://news.ycombinator.com/item?id=48321906 Points: 2 # Comments: 0
系统梳理LLM预训练数据暴露的成员推理、数据污染与安全影响，已被NLDB 2025接收，是研究数据隐私的必备综述。
arXiv:2605.26133v1 Announce Type: cross Abstract: Large Language Models (LLMs) have become the predominant paradigm in NLP, advancing both research an…
代码大模型的数据污染检测新框架，语义感知实现细粒度识别，提升模型评估可靠性
arXiv:2605.24079v1 Announce Type: cross Abstract: Data contamination is a known threat to the reliability of model evaluation. However, it remains und…
可证明的联合去污染方法，解决多LLM基准测试中的数据污染难题
arXiv:2605.21543v1 Announce Type: new Abstract: Benchmark data contamination has become a central challenge in LLM evaluation: when evaluation example…
最新研究：LLM在税法推理中存在数据污染风险，别被“假懂”骗了！
arXiv:2605.16052v1 Announce Type: new Abstract: Recent advances in large language models (LLMs) have significantly enhanced automated legal reasoning.…