Enhancing LLM Language Adaption through Cross-lingual In-Context Pre-training

• URL: http://arxiv.org/abs/2504.20484v2
• Date: Fri, 19 Sep 2025 04:09:02 GMT
• Title: Enhancing LLM Language Adaption through Cross-lingual In-Context Pre-training
• Authors: Linjuan Wu, Haoran Wei, Huan Lin, Tianhao Li, Baosong Yang, Fei Huang, Weiming Lu,
• Abstract summary: Cross-lingual In-context Pre-training (CrossIC-PT) is a simple and scalable approach that enhances cross-lingual transfer.<n>We construct CrossIC-PT samples by interleaving semantic-related bilingual Wikipedia documents into a single context window.<n> Experimental results demonstrate that CrossIC-PT improves multilingual performance on three models across six target languages.
• Score: 57.62126373849383
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models (LLMs) exhibit remarkable multilingual capabilities despite English-dominated pre-training, attributed to cross-lingual mechanisms during pre-training. Existing methods for enhancing cross-lingual transfer remain constrained by parallel resources, suffering from limited linguistic and domain coverage. We propose Cross-lingual In-context Pre-training (CrossIC-PT), a simple and scalable approach that enhances cross-lingual transfer by leveraging semantically related bilingual texts via simple next-word prediction. We construct CrossIC-PT samples by interleaving semantic-related bilingual Wikipedia documents into a single context window. To access window size constraints, we implement a systematic segmentation policy to split long bilingual document pairs into chunks while adjusting the sliding window mechanism to preserve contextual coherence. We further extend data availability through a semantic retrieval framework to construct CrossIC-PT samples from web-crawled corpus. Experimental results demonstrate that CrossIC-PT improves multilingual performance on three models (Llama-3.1-8B, Qwen2.5-7B, and Qwen2.5-1.5B) across six target languages, yielding performance gains of 3.79%, 3.99%, and 1.95%, respectively, with additional improvements after data augmentation.

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