[1] https://en.wikipedia.org/wiki/Lojban
[2] Someone speaking it: https://www.youtube.com/watch?v=lxQjwbUiM9w
Ah, the Lisp curse. Here we go again.
coincidently, the 80s AI bubble crashed partly because Lisp dialetcts aren't inter-changable.
But some random in-house DSL? Doubt it.
Context pollution is a bigger problem.
E.g., those SKILL.md files that are tens of kilobytes long, as if being exhaustively verbose and rambling will somehow make the LLM smarter. (It won't, it will just dilute the context with irrelevant stuff.)
In my mind this is solving different problems. We want it to parse out our intent from ambiguous semantics because that's how humans actually think and speak. The ones who think they don't are simply unaware of themselves.
If we create this terse and unambiguous language for LLMs, it seems likely to me that they would benefit most from using it with each other, not with humans. Further, they already kind of do this with programming languages which are, more or less, terse and unambiguous expression engines for working with computers. How would we meaningfully improve on this, with enough training data to do so?
I'm asking sincerely and not rhetorically because I'm under no illusion that I understand this or know any better.
We literally have proof that an iron age ontology of meaning as represented in Chinese characters is 40% more efficient than naive statistical analysis over a semi phonetic language and we still are acting like more compute will solve all our problems.
Can you elaborate? I think you're talking about https://github.com/PastaPastaPasta/llm-chinese-english , but I read those findings as far more nuanced and ambiguous than what you seem to be claiming here.
Post a link because until you do, I’m almost certain this is pseudoscientific crankery.
Chinese characters are not an “iron age ontology of meaning” nor anything close to that.
Also please cite the specific results in centuries-old “language theory” that you’re referring to. Did Saussure have something to say about LLMs? Or someone even older?
Some languages don't have this kind of vocabulary, because there aren't enough speakers that deal with technical things in a given area (and those that do, use another language to communicate)
Setting aside the problem of training, why bother prompting if you’re going to specify things so tightly that it resembles code?
The uninitiated can continue trying to clumsily refer to the same concepts, but with 100x the tokens, as they lack the same level of precision in their prompting. Anyone wanting to maximize their LLM productivity will start speaking in this unambiguous, highly information-dense dialect that optimizes their token usage and LLM spend...