arXiv cs.CL· Ansar Aynetdinov, Patrick Haller, Alan Akbik·· 14 小时前AI 评分34
重复优于多样性：面向样本高效德语语言建模的高信号数据过滤
Repetition over Diversity: High-Signal Data Filtering for Sample-Efficient German Language Modeling
AI 导读
对 500M 德语网页文档施加分层质量过滤后，多轮重复高质量子集的训练效果持续优于在更大、过滤更少的语料上单轮训练，且差距在 7 个 epoch 后依然存在。研究团队开源了德语模型 Boldt 及清洗后的评测基准，称其在比同类模型少 10-360 倍 token 的训练量下取得 SOTA 结果。
正文 · 原文
Abstract:Recent research has shown that filtering massive English web corpora into high-quality subsets significantly improves training efficiency. However, for high-resource non-English languages like German, French, or Japanese, aggressive filtering creates a strategic dilemma: should practitioners prioritize diversity by training once on large amounts of lightly filtered web data, or prioritize quality by strictly filtering for a high-quality core and repeating it over multiple epochs? We investigate this trade-off for German by applying hierarchical quality filters to 500M web documents, comparing multi-epoch training on filtered subsets against single-pass training on a diverse corpus. Our experiments across multiple model scales and token budgets show that repeating high-quality data consistently outperforms single-pass training on larger, less filtered sets. Notably, the performance gap persists even after 7 epochs. Our findings suggest that for non-English LLMs, semantic concentration through quality filtering offers a more viable path to efficient language modeling than simply maximizing unique data volume. We release our German language models (called Boldt), as well as our cleaned evaluation benchmarks to the research community. Our experiments indicate that they achieve state-of-the-art results despite training on 10-360x fewer tokens than comparable models.
| Comments: | Accepted to EMNLP 2026 (Main) |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2604.28075 [cs.CL] |
| (or arXiv:2604.28075v3 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2604.28075 arXiv-issued DOI via DataCite |
Submission history
From: Ansar Aynetdinov [view email]
[v1]
Thu, 30 Apr 2026 16:21:28 UTC (921 KB)
[v2]
Fri, 1 May 2026 13:52:15 UTC (483 KB)
[v3]
Fri, 23 Oct 2026 19:22:47 UTC (491 KB)
来源：arXiv cs.CL · arxiv.org