English(EN) Quantifying Error Tolerance in Synthetic Data: An Atomic-level Operand vs. Operator Perturbation Study

新框架ATOM改进LLM的合成数据

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-01 04:00

研究人员推出ATOM，一个旨在提高用于训练大型语言模型（LLM）的合成数据质量的框架。ATOM区分了数据操作数中的良性扰动和操作符中的关键扰动，发现模型对操作符错误更敏感。通过优先考虑操作符多样性而非操作数精度，ATOM合成的数据在性能上优于现有方法。 AI

影响这项研究通过提高合成数据的质量，有望实现更高效、更有效的大型语言模型训练。

排序理由该集群包含一篇学术论文，详细介绍了用于合成数据生成的新框架。[lever_c_demoted from research: ic=1 ai=1.0]

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

本文如何被排名

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Newsworthiness bucket: Tool; 该集群包含一篇学术论文，详细介绍了用于合成数据生成的新框架。[lever_c_demoted from research: ic=1 ai=1.0]
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arXiv cs.CL TIER_1 English(EN) · Jiaxiang Liu, Chenhao Yuan, Shuwen Xu, Boxuan Xing, Xiusheng Huang, Yinhao Xu, Hao Liu, Wenhao Teng, Xiangwen Liao, Pengfei Cao, Jun Zhao, Kang Liu · 2026-09-01 04:00

量化合成数据中的误差容忍度：原子级操作数与操作符扰动研究

arXiv:2608.29144v1 Announce Type: new Abstract: Synthetic data generation has become a cornerstone for advancing large language models. However, the lack of the quantitative analysis for error tolerance became a critical bottleneck. Consequently, current filtering strategies fluc…