README.md

---
license: mit
task_categories:
- question-answering
language:
- fr
tags:
- medical
- biology
size_categories:
- 10K<n<100K
---

# 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 **416 401 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 **416 401 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://github.com/biomedical-translation-corpora/corpora)** corpus.  
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