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English(EN) DataFoundry: Evolving Data Preparators via Recursive Self-Improvement

DataFoundry 框架演进 LLM 训练数据准备器

研究人员开发了 DataFoundry,一个旨在提高大型语言模型训练数据质量的新颖框架。与在生成后过滤数据的传统方法不同,DataFoundry 通过递归自我改进来专注于演进数据准备过程本身。该框架利用了模块化技能(Skills-as-Modules)架构,其中控制器管理模块化技能以创建可执行运行时,使用试点数据集识别缺陷,并根据诊断反馈优化准备组件。在数学、金融、法律和医学等各个领域的 DataPrep-Bench 上进行的评估表明,与基线方法相比,DataFoundry 生成的数据在下游应用中具有更高的效用。 AI

影响 提高了 LLM 训练数据的质量,可能导致在各个领域中更强大、更可靠的模型。

排序理由 这是一篇详细介绍 LLM 数据准备新框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

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

DataFoundry 框架演进 LLM 训练数据准备器

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这是一篇详细介绍 LLM 数据准备新框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CL TIER_1 English(EN) · Cehao Yang, Xiaojun Wu, Xueyuan Lin, Chengjin Xu, Xuhui Jiang, Hui Xiong, Jian Guo ·

    DataFoundry:通过递归自我改进来改进数据准备器

    arXiv:2608.29966v1 Announce Type: new Abstract: Domain adaptation of large language models increasingly depends on constructing high-quality training data, yet existing data-preparation pipelines typically address quality only after generation through post-hoc filtering. This cre…