Scaling Training Data Effects on Cross-Lingual Generalization in Low-Resource Alignment

This report synthesises findings from 14 peer-reviewed papers addressing the following research question: What is the impact of scaling training data volume on the cross-lingual generalization gap of offline alignment techniques versus standard SFT in low-resource language settings. Abstract The rapid evolution of large language models (LLMs) has driven a transformative shift in artificial intelligence (AI), reshaping both research paradigms and practical applications. Distinguished from their predecessors by unprecedented scale and advanced capabilities. 11 claims were extracted from source literature; 9 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 7.5/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research goal: What is the impact of scaling training data volume on the cross-lingual generalization gap of offline alignment techniques versus standard SFT in low-resource language settings?

Autonomous literature synthesis. Automated review score: 7.5/10. Full text and citation available at Assignee Research.