Multilingual, multimodal, multiagent AI systems

SSMs are more memory-efficient than transformers, allowing them to handle longer sequences of text without running into memory constraints. This is particularly beneficial for translating long documents or maintaining context across multiple sentences.

The computational complexity of SSMs scales linearly with the input length, making them faster and more efficient for processing long sequences compared to transformers, which have quadratic scaling.

SSMs offer better interpretability compared to transformers, potentially providing insights into the model's decision-making process and allowing for easier debugging and improvement.

RAG models can access and leverage external knowledge sources, like databases or the internet, to improve the accuracy and relevance of translations. This is particularly useful for translating technical or specialized content that requires domain-specific knowledge.

By relying on retrieved information, RAG models are less likely to generate factually incorrect or nonsensical translations, a common issue with purely generative models.  

RAG models can be easily adapted to new domains or languages by providing them with relevant knowledge sources, making them more versatile and flexible than traditional translation models.