Yes, this was my logic. A model trained on bird song may make birds turn their heads when it babbles something in their frame of reference, but if you want to "use LLM" to make it human comprehensible you need a human LLM at some point, which will impose humanity on its input regardless of what you feed it, because all a human LLM can represent is human language. A human LLM can't help but make whatever it is used on human. A bird LLM would have the same effect; no matter what you feed it, you'll get something in the bird input's space.

Not sure you're really getting the idea. In this scenario, there wouldn't be human llm and dolphin llm, you would train human speech and dolphin communication in one model. sound tokens are sound tokens. pre-trained transformers don't discriminate.

The concern would be that it would gnerate a bunch of spurious correlations between the two. You would be mixing two kinds of datasets, where it’s not clear that the dolphin sounds are linguistic. It’s also not clear that LLMs would correctly map human language to a non-human one. Depends on your theory of meaning. Wittgenstein would say if a lion could talk, we would not understand it, because lmeaning is use, and lions would use language differently than humans.