Improving Translation Quality by Selecting Better Data for LLM Fine-Tuning: A Comparative Analysis

Felipe Ribeiro Fujita de Mello; Hideyuki Takada

doi:10.1109/bigdata66926.2025.11402145

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arXiv · 2512.11388

Improving Translation Quality by Selecting Better Data for LLM Fine-Tuning: A Comparative Analysis

Felipe Ribeiro Fujita de Mello·Hideyuki Takada

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Abstract

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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Felipe Ribeiro Fujita de Mello, Hideyuki Takada. 2025-12-12. Improving Translation Quality by Selecting Better Data for LLM Fine-Tuning: A Comparative Analysis. https://doi.org/10.1109/bigdata66926.2025.11402145

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