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Comment by zer00eyz | Hacker News Reader

the sounds for man, and the word man can be mapped. The concept of man can be mapped from English to Chinese. Given enough of these clues, and a large enough data set it's not shocking that models get decent at inference between languages.

But whales? What we are (probably) proposing is putting two languages side by side with NO context and saying "figure it out".

Here is a great example of people and language and perception: https://news.mit.edu/2023/how-blue-and-green-appeared-langua...

Im fairly confident that unless we give an LLM a LOT of context between data sets to work with that it won't make much progress at all.

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>the sounds for man, and the word man can be mapped. The concept of man can be mapped from English to Chinese. Given enough of these clues, and a large enough data set it's not shocking that models get decent at inference between languages.

1. Many such concepts cannot be directly mapped between different languages especially with distant language pairs.

2. Language Models and Image models even if both are only trained on their respective modality learn structural representations so similar, you can connect them with a simple linear layer. That's it. Entirely different modalities https://arxiv.org/abs/2209.15162

https://arxiv.org/abs/2304.08485

I think you are severely underestimating the extent to which representations can group in neural networks.

They're mammals, they're social animals, they see. You're assuming a level of alienness we don't have the knowledge or understanding to truly ascertain.

couldn’t we approach it like (my understanding of) code breaking worked back in ww2? meta observations of recurrent symbols, like time, start/end message, etc reveal certain consistencies. then we fill in gaps through possible coherences.

while ww2 code breaking contained other human contexts, perhaps like-context observations could allow for certain categories that might reveal linguistic insights of whales. like whale sounds observed consistently near boats, divers, danger, eating, play, etc.

no clue about how LLM/AI systems process meta contextual training data inputs (is this even a valid phrase about AI?! haha) but i hope whale linguists have considered such an approach.

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