The training of the LLM itself would still use the human language. But you could add an extra channel that's never given any text or direct dataset training. Keep it purely a connection between hidden layers of different instances of LLM and train using the usual loss of perplexity or similar metric.

The interesting thing then would be - does it converge to similar embedding space as the input, or can LLMs create a more efficient "language".