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arXiv:2608. 15964v1 Announce Type: cross Abstract: Language-specific competency (LSC) is the phenomenon of a language model performing better or worse depending on the language of the prompt.
The paper explores how to leverage parallel corpora for multilingual pretraining by concatenating each document with its translation in the same training sequence, a method called Parallel Pretraining (PPT). Compared to standard pretraining (SPT), PPT alone does not consistently improve language understanding, but it builds a stronger cross‑lingual foundation. A two‑stage approach—first PPT to establish cross‑lingual knowledge, then SPT to fine‑tune for standard language modeling—consistently outperforms standard pretraining in both high‑resource and low‑resource settings.
arXiv:2607.00890v2 Announce Type: replace Abstract: Open web-scale pre-training corpora remain concentrated in English, limiting multilingual LLM development. We introduce MultiSynt/MT, an open synth...
Open web-scale pre-training corpora remain concentrated in English, limiting multilingual LLM development. We introduce MultiSynt/MT, an open synthetic parallel corpus with approximately 4.
arXiv:2608. 11715v1 Announce Type: cross Abstract: The reliability of Large Language Models (LLMs) for API calling degrades in multilingual settings.
EuroAlpaca presents a task‑preserving localisation pipeline that translates English instruction‑tuning data into 50 European languages while maintaining task‑critical constraints. The method uses field‑wise machine translation or reconstructs task‑equivalent target‑language instances, followed by validation of coherence and consistency. Experiments show that EuroAlpaca improves instruction‑following accuracy by 12.9% over a baseline and outperforms direct translation on ROUGE‑L and F‑BERT metrics.