I think some earlier NLP applications have something called "Unknown token", which they will replace any unseen word. But for recent implementations, I don't think they are being used anymore.
It still baffles me why such stochastic parrot / next token predictor, will recognize these "Unseen combinations of tokens" and reuse them in response.
Everything falls into place once you understand that LLMs are indeed learning hierarchical concepts inherent in the structured data it has been trained on. These concepts exist in a high dimensional latent space. Within this space is the concept of nonsense/gibberish/placeholder, which your sequence of unseen tokens map to. Then it combines this with the concept of SQL tables, resulting in hopefully the intended answer.
It still baffles me why such stochastic parrot / next token predictor, will recognize these "Unseen combinations of tokens" and reuse them in response.