Improving Translation Quality by Selecting Better Data for LLM Fine-Tuning: A Comparative Analysis
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866912759858331648 |
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| author | de Mello, Felipe Ribeiro Fujita Takada, Hideyuki |
| author_facet | de Mello, Felipe Ribeiro Fujita Takada, Hideyuki |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_11388 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Improving Translation Quality by Selecting Better Data for LLM Fine-Tuning: A Comparative Analysis de Mello, Felipe Ribeiro Fujita Takada, Hideyuki Computation and Language 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. |
| title | Improving Translation Quality by Selecting Better Data for LLM Fine-Tuning: A Comparative Analysis |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2512.11388 |