What if we could test whether language models truly understand meaning, rather than just matching patterns? Here's a simple thought experiment:
If successful, this would suggest the model has learned to understand the underlying meanings, not just statistical patterns between languages. Theoretically, if Language A and Language B each form true mappings (MA and MB) to the same concept space R', then the model should be able to perform translation through the composition MA·MB^(-1), effectively going from Language A to concepts and then to Language B, without ever seeing parallel examples. This emergent translation capability would be a strong indicator of genuine semantic understanding, as it requires the model to have internalized the relationship between symbols and meanings in each language independently.
This approach could help distinguish between:
It's like testing if someone really understands two languages versus just memorizing a translation dictionary.
Concept: "red circle"
Without ever showing the model that "zix-kol" = "nare-tup", can it figure out the translation by understanding that both phrases refer to the same concept?
As an undergraduate student outside the AI research community, I acknowledge:
I'm sharing this idea in hopes that:
I'm an engineering student interested in AI understanding and alignment. While I may not have the resources to develop this idea fully, I hope sharing it might spark useful discussions or inspire more developed approaches.
If you have thoughts, suggestions, or see potential in this idea, I'd love to hear from you. Please feel free to comment or reach out.