Datasets:

MedInjection-FR
/

Translated

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 size_categories:
 - 10K<n<100K
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 size_categories:
 - 10K<n<100K
 ---
+---
+pretty_name: MedInjection-FR — Translated Subset
+language:
+- fr
+license: mit
+task_categories:
+- translation
+- question-answering
+size_categories:
+- 300K<n<500K
+---
+# MedInjection-FR — Translated Subset 🌍
+## Summary
+The **Translated** component of **MedInjection-FR** adapts large-scale **English biomedical instruction datasets** into French through high-quality automatic translation.
+It represents the most extensive part of the collection, comprising **418 061 instruction–response pairs**, and provides a bridge between English biomedical resources and French medical instruction tuning.
+This subset was designed to ensure **broad domain coverage** while maintaining **semantic and linguistic fidelity**, enabling robust cross-lingual transfer and comparative evaluation between native, synthetic, and translated supervision sources.
+## Motivation
+The scarcity of large-scale French biomedical instruction data limits the capacity of LLMs to generalize across complex medical domains.
+To address this, the Translated subset leverages **trusted English benchmarks**—spanning medicine, biology, psychology, and clinical knowledge—and systematically translates them into French while preserving the structure of instruction–response pairs.
+This allows for rigorous experiments on **cross-lingual instruction adaptation**, complementing the native and synthetic subsets of MedInjection-FR.
+## Composition
+To expand data coverage while maintaining linguistic fidelity, a large collection of **English biomedical instruction datasets** was translated into French using **[Gemini 2.0 Flash](https://blog.google/products/gemini/google-gemini-2/)** and **[GPT-4o-mini](https://openai.com/research/gpt-4o)**.
+The translated component comprises **418 061 instruction–response pairs** derived from several established English resources:
+- **[MedQA](https://arxiv.org/abs/2009.13081)** – medical board–style multiple-choice QA.
+- **[PubMedQA](https://aclanthology.org/D19-1259/)**  – factoid biomedical QA from PubMed abstracts.
+- **[MedMCQA](https://proceedings.mlr.press/v174/pal22a.html)**  – clinical multiple-choice QA covering 21 medical specialties.
+- **[MMLU](https://arxiv.org/abs/2009.03300)**  – six medical categories (e.g., anatomy, clinical knowledge, college biology, college medicine, medical genetics, professional medicine).
+- **[K-QA](https://aclanthology.org/2024.bionlp-1.22/)**  – open-ended biomedical question answering for reasoning over scientific text.
+- **[MMLU-PRO](https://arxiv.org/abs/2406.01574)**  – professional-level QA across psychology, biology, and health domains.
+- **[MedXpertQA](https://arxiv.org/abs/2501.18362)**  – clinical reasoning dataset focusing on multi-hop and expert-level diagnostic questions.
+To assess translation quality, outputs were evaluated using **BLEU** and **COMET** on the **[WMT 2024 Biomedical Translation Task](https://www.statmt.org/wmt24/biomedical-task.html)** corpus \cite{neves-etal-2024-findings}.
+Both *Gemini 2.0 Flash* and *GPT-4o-mini* achieved results comparable to the **best WMT 2024 system**, indicating that the Translated subset maintains high semantic fidelity and linguistic quality suitable for French biomedical instruction tuning.
+## Use
+Intended for:
+- Fine-tuning French biomedical LLMs with translated instruction–response pairs
+- Studying cross-lingual transfer and translation quality in instruction tuning
+- Evaluating the interplay between translation fidelity and domain adaptation performance