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

arXiv: https://arxiv.org/abs/2512.11388 · HTML: https://mlsanity.com/paper/2512.11388/

## 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.

## Builds on (most cited first, 17 of 17)

- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Qwen3 Technical Report](https://mlsanity.com/paper/2505.09388/) (2025)
- [Mistral 7B](https://mlsanity.com/paper/2310.06825/) (2023)
- [Gemma: Open Models Based on Gemini Research and Technology](https://mlsanity.com/paper/2403.08295/) (2024)
- [An Empirical Study of Example Forgetting during Deep Neural Network Learning](https://mlsanity.com/paper/1812.05159/) (2018)
- [Data Selection for Language Models via Importance Resampling](https://mlsanity.com/paper/2302.03169/) (2023)
- [A Survey on Data Selection for Language Models](https://mlsanity.com/paper/2402.16827/) (2024)
- [CometKiwi: IST-Unbabel 2022 Submission for the Quality Estimation Shared Task](https://mlsanity.com/paper/2209.06243/) (2022)
- [QuRating: Selecting High-Quality Data for Training Language Models](https://mlsanity.com/paper/2402.09739/) (2024)
- [WMT24++: Expanding the Language Coverage of WMT24 to 55 Languages & Dialects](https://mlsanity.com/paper/2502.12404/) (2025)
- [Predictive Data Selection: The Data That Predicts Is the Data That Teaches](https://mlsanity.com/paper/2503.00808/) (2025)
- [Fine-Tuning Large Language Models to Translate: Will a Touch of Noisy Data in Misaligned Languages Suffice?](https://mlsanity.com/paper/2404.14122/) (2024)
- [A Comparative Study on TF-IDF feature Weighting Method and its Analysis using Unstructured Dataset](https://mlsanity.com/paper/2308.04037/) (2023)
- [Large Language Models are Good Multi-lingual Learners : When LLMs Meet Cross-lingual Prompts](https://mlsanity.com/paper/2409.11056/) (2024)
- [Swift Cross-Dataset Pruning: Enhancing Fine-Tuning Efficiency in Natural Language Understanding](https://mlsanity.com/paper/2501.02432/) (2025)
- [Exploring Parameter-Efficient Fine-Tuning and Backtranslation for the WMT 25 General Translation Task](https://mlsanity.com/paper/2511.12109/) (2025)

## 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.