More advanced models, like Transformer decoders or T5 encoders with more sophisticated pre-training, could offer a solution. Moreover, due to the stochastic nature of the generation process, guiding the model to match compositions when many atoms are in the unit cell can be challenging. For that reason, we limited the textual descriptions to composition and crystal systems, but future work will need to handle more general and specific applications like abstracts or full papers from the scientific literature as textual inputs to broaden the scope of text-driven models.