Fluent Alignment with Disfluent Judges: Post-training for Lower-resource Languages

1. 1Identify lower-resource languages with limited datasets and instruction-tuned language models
2. 2Develop post-training methods to preserve fluency of language models
3. 3Use preference optimization to align language models with disfluent reward models
4. 4Evaluate model performance on fluency and accuracy metrics

NLP researchers and AI engineers working on language models for lower-resource languages can benefit from this method to improve model fluency and performance

💡 Post-training methods can preserve fluency of language models even with disfluent reward models

📚 Improve language model fluency for lower-resource languages with post-training methods! 💡