MedInjection-FR: Exploring the Role of Native, Synthetic, and Translated Data in Biomedical Instruction Tuning
MedInjection-FR: Exploring the Role of Native, Synthetic, and Translated Data in Biomedical Instruction Tuning explores MedInjection-FR provides a French biomedical instruction dataset and instruction tuning recipes to adapt LLMs for medical question answering.. Commercial viability score: 7/10 in Medical AI.
BUILDER'S SANDBOX
Build This Paper
Use an AI coding agent to implement this research.
OpenAI CodexAI Agent
Lightweight coding agent in your terminal.
Claude CodeAI Agent
Agentic coding tool for terminal workflows.
AntiGravity IDEScaffolding
AI agent mindset installer and workflow scaffolder.
CursorIDE
AI-first code editor built on VS Code.
VS CodeIDE
Free, open-source editor by Microsoft.
Recommended Stack
Startup Essentials
Estimated $9K - $13K over 6-10 weeks.
See exactly what it costs to build this -- with 3 comparable funded startups.
7-day free trial. Cancel anytime.
Discover the researchers behind this paper and find similar experts.
7-day free trial. Cancel anytime.
Related Papers
- Builds On ThisEvaluating Fine-Tuned LLM Model For Medical Transcription With Small Low-Resource Languages Validated DatasetViability: 5.0
- Builds On ThisLearning from Child-Directed Speech in Two-Language Scenarios: A French-English Case StudyViability: 2.0
- Builds On ThisMerge and Conquer: Instructing Multilingual Models by Adding Target Language WeightsViability: 5.0
- Builds On ThisComparing Natural and Synthetic Structured Data: A Study of the Passive Verb Alternation in French and ItalianViability: 4.0
- Prior Work