# MedInjection-FR: Exploring the Role of Native, Synthetic, and Translated Data in Biomedical Instruction Tuning

arXiv: https://arxiv.org/abs/2603.06905 · HTML: https://mlsanity.com/paper/2603.06905/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 52 references that are in the map.
- First posted on arXiv 6 Mar 2026.

## Builds on (most cited first, 16 of 16)

- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Self-Instruct: Aligning Language Models with Self-Generated Instructions](https://mlsanity.com/paper/2212.10560/) (2022)
- [MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark](https://mlsanity.com/paper/2406.01574/) (2024)
- [What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams](https://mlsanity.com/paper/2009.13081/) (2020)
- [PubMedQA: A Dataset for Biomedical Research Question Answering](https://mlsanity.com/paper/1909.06146/) (2019)
- [Justice or Prejudice? Quantifying Biases in LLM-as-a-Judge](https://mlsanity.com/paper/2410.02736/) (2024)
- [Understanding Catastrophic Forgetting in Language Models via Implicit Inference](https://mlsanity.com/paper/2309.10105/) (2023)
- [AlpaCare:Instruction-tuned Large Language Models for Medical Application](https://mlsanity.com/paper/2310.14558/) (2023)
- [K-QA: A Real-World Medical Q&A Benchmark](https://mlsanity.com/paper/2401.14493/) (2024)
- [FrenchMedMCQA: A French Multiple-Choice Question Answering Dataset for Medical domain](https://mlsanity.com/paper/2304.04280/) (2023)
- [DrBenchmark: A Large Language Understanding Evaluation Benchmark for French Biomedical Domain](https://mlsanity.com/paper/2402.13432/) (2024)
- [Beyond Fine-tuning: Unleashing the Potential of Continuous Pretraining for Clinical LLMs](https://mlsanity.com/paper/2409.14988/) (2024)
- [MORQA: Benchmarking Evaluation Metrics for Medical Open-Ended Question Answering](https://mlsanity.com/paper/2509.12405/) (2025)
- [Aligning Instruction Tuning with Pre-training](https://mlsanity.com/paper/2501.09368/) (2025)
- [MediQAl: A French Medical Question Answering Dataset for Knowledge and Reasoning Evaluation](https://mlsanity.com/paper/2507.20917/) (2025)
- [InstaTrans: An Instruction-Aware Translation Framework for Non-English Instruction Datasets](https://mlsanity.com/paper/2410.01512/) (2024)

## Built on by (most cited first, 1 of 1)

- [Trade-offs in Medical LLM Adaptation: An Empirical Study in French QA](https://mlsanity.com/paper/2606.19266/) (2026)

## Abstract (arXiv)

Instruction tuning has become essential for adapting large language models (LLMs) to follow domain-specific prompts. Yet, in specialized fields such as medicine, the scarcity of high-quality French instruction data limits effective supervision. To address this gap, we introduce MedInjection-FR, a large-scale French biomedical instruction dataset comprising 571K instruction-response pairs drawn from three complementary sources: native, synthetic, and translated data. We design a controlled experimental framework to systematically assess how data provenance affects instruction tuning, using Qwen-4B-Instruct fine-tuned across seven configurations combining these sources. Results show that native data yield the strongest performance, while mixed setups, particularly native and translated, provide complementary benefits. Synthetic data alone remains less effective but contributes positively when balanced with native supervision. Evaluation on open-ended QA combines automatic metrics, LLM-as-a-judge assessment, and human expert review; although LLM-based judgments correlate best with human ratings, they show sensitivity to verbosity. These findings highlight that data authenticity and diversity jointly shape downstream adaptation and that heterogeneous supervision can mitigate the scarcity of native French medical instructions.