Year: 2026
Type: conference-paper
Abstract: Large Language Models (LLMs) have recently exploded in popularity, often matching or outperforming human abilities on many tasks.One of the key factors in training LLMs is the availability and curation of high-quality data.Data quality is especially crucial for underrepresented languages, where high-quality corpora are scarce.In this work, we study the characteristics and coverage of Romanian pretraining corpora and we examine how they differ from English data.By training a lightweight multitask model on carefully LLM-annotated Romanian texts, we are able to analyze and perform multi-level filtering (e.g., educational value, topic, format) to generate high-quality pretraining datasets.Our experiments show noteworthy trends in the topics present in Romanian and English data, while also proving the effectiveness of filtering data through improved LLM pretraining performance across multiple benchmarks.
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
Funder: Health Resources in Action
Award: G4414739080
OpenAlex ID: W7140142014