mlsanityData to 14 Oct 2026

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

arXiv 2512.11388 ↗First posted 12 Dec 2025cs.CLcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 17 of its 23 references that are in the map. First posted on arXiv 12 Dec 2025.

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References · 17

  1. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,380
  2. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
  3. 2025Qwen3 Technical Reportcited by 8,820
  4. 2023Mistral 7Bcited by 3,164
  5. 2024Gemma: Open Models Based on Gemini Research and Technologycited by 916
  6. 2018An Empirical Study of Example Forgetting during Deep Neural Network Learningcited by 678
  7. 2023Data Selection for Language Models via Importance Resamplingcited by 329
  8. 2024A Survey on Data Selection for Language Modelscited by 236
  9. 2022CometKiwi: IST-Unbabel 2022 Submission for the Quality Estimation Shared Taskcited by 207
  10. 2024QuRating: Selecting High-Quality Data for Training Language Modelscited by 157
  11. 2025WMT24++: Expanding the Language Coverage of WMT24 to 55 Languages & Dialectscited by 79
  12. 2025Predictive Data Selection: The Data That Predicts Is the Data That Teachescited by 21
5 more
  1. 2024Fine-Tuning Large Language Models to Translate: Will a Touch of Noisy Data in Misaligned Languages Suffice?cited by 17
  2. 2023A Comparative Study on TF-IDF feature Weighting Method and its Analysis using Unstructured Datasetcited by 10
  3. 2024Large Language Models are Good Multi-lingual Learners : When LLMs Meet Cross-lingual Promptscited by 10
  4. 2025Swift Cross-Dataset Pruning: Enhancing Fine-Tuning Efficiency in Natural Language Understandingcited by 4
  5. 2025Exploring Parameter-Efficient Fine-Tuning and Backtranslation for the WMT 25 General Translation Taskcited by 1

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