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Improving Translation Quality by Selecting Better Data for LLM Fine-Tuning: A Comparative Analysis
Felipe Ribeiro Fujita de Mello, Hideyuki Takada · 2025-12-12 · via cs.CL updates on arXiv.org

We investigated the impact of data selection on machine translation fine-tuning for open LLMs. Using Japanese-English corpora, we compare five selectors: TF-IDF, COMET Kiwi, QuRate, FD-Score, and random selection, under controlled training conditions. We observed that semantic selectors consistently outperform lexical and geometry-based heuristics, and that even when the selected data differ by less than 3%, the impact on model performance is substantial, underscoring the sensitivity of fine-tuning to data quality.

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