Language Adapter

Language adapters are small, trainable modules added to pre-trained language models to efficiently adapt them to new languages or tasks, minimizing the need to retrain the entire model. Current research focuses on optimizing adapter architectures, exploring their behavior within the larger model (e.g., analyzing their interaction with different layers), and investigating their effectiveness in low-resource settings, particularly for under-represented languages. This approach offers a parameter-efficient solution for multilingual natural language processing, improving performance in tasks like speech-to-text, machine translation, and natural language understanding while reducing computational costs and data requirements.