arXiv:2604.28075v3 Announce Type: replace-cross
Abstract: Recent research has shown that filtering massive English web corpora into high-quality subsets significantly improves training efficiency. Ho...
By Ansar Aynetdinov, Patrick Haller, Alan Akbik
The paper proposes a multilingual adaptation of an English quality classifier to select high‑quality training data for large language model pretraining across more than 100 languages. The method trains a small multi‑layer perceptron on Transformer embeddings, using machine‑translated text scored by the English classifier as labels. Experiments on 1B, 3B, and 8B parameter models show that the adapted classifier preserves benchmark performance and regional knowledge, and it generalizes to unseen languages by learning the original scoring criteria.
By Vinko Sabol\v{c}ec, Bettina Messmer, Yassine Turki, Martin Jaggi
arXiv:2604. 13977v2 Announce Type: replace-cross Abstract: Synthetic data is a standard component in training large language models, yet systematic comparisons across design dimensions, including rephrasing strategy, generator model, and source data, remain absent.
By Joel Niklaus, Atsuki Yamaguchi, Michal \v{S}tef\'anik, Guilherme Penedo, Hynek Kydl\'i\v{c}ek, Elie Bakouch, Lewis Tunstall, Edward Emanuel Beeching, Thibaud Frere, Colin Raffel, Leandro von Werra, Thomas Wolf
arXiv:2603.12165v3 Announce Type: replace
Abstract: Synthetic data has become essential for training code generation models, yet it introduces significant noise and hallucinations that are difficult...
By Jiayin Lei, Ming Ma, Yunxi Duan, Chenxi Li, Tianming Yang
The study examines how to allocate web‑crawling resources to gather higher‑quality data for large language model pretraining. By combining document‑level quality scores, web‑graph centrality, and existing crawl coverage, it finds that most variation in quality is between hosts and that sampling a few pages from a host can predict the quality of the rest. The results suggest that depth‑based sampling of high‑scoring hosts yields far more high‑quality pages per fetch than random or centrality‑based strategies, while overall host coverage shows little correlation with quality.
By Malte Ostendorff
arXiv:2606. 11499v1 Announce Type: cross Abstract: The performance of modern language models depends critically on pretraining data composition.
By Vedant Badoni, Danqi Chen, Xinyi Wang