AuthorMix: Modular Authorship Style Transfer via Layer-wise Adapter Mixing

1. 1Train a base model on a large corpus
2. 2Add modular adapters for each target author style
3. 3Mix adapters to transfer style while preserving meaning
4. 4Fine-tune the model for specific target authors

NLP researchers and AI engineers benefit from AuthorMix as it provides a flexible and efficient approach to style transfer, allowing for target-specific adaptation without sacrificing meaning preservation

💡 Modular adapters enable efficient and flexible style transfer without sacrificing meaning preservation

📚 AuthorMix: Modular authorship style transfer via layer-wise adapter mixing! 💡