Troubleshooting a Text Simplification Model

A team is developing a model to simplify complex medical jargon into plain language for patients. They have successfully trained an encoder-decoder model on a large dataset of medical text and its simplified version. However, when they test the model, they find it frequently produces outputs that are grammatically correct and simple, but factually inaccurate (e.g., changing 'benign tumor' to 'harmless growth' but 'malignant tumor' to 'minor lump'). What is the most likely cause of this specific type of failure?

You are tasked with creating a text simplification tool using a sequence-to-sequence learning approach. Arrange the following core steps in the correct chronological order, from initial data preparation to generating a final output.