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s/preserve/hallucinate/ The next few decades are going to be really, really weird.
Hallucinations rarely make up invalid grammar or invent non-existent words, what we're concerned about is facts, which isn't relevant at all when the goal is language preservation.
s/preserve/hallucinate/ The next few decades are going to be really, really weird.
This take us overly cynical in this case—the original and primary use case for LLMs is to model languages in a comprehensive way. It's literally in the name. Hallucinations rarely make up invalid grammar or invent non-existent words, what we're concerned about is facts, which isn't relevant at all when the goal is language preservation.
I've seen LLMs hallucinate nonexistent things in programming languages. It's hard to believe it won't do the same to human ones.
Earlier quoted context omitted.
This take us overly cynical in this case—the original and primary use case for LLMs is to model languages in a comprehensive way. It's literally in the name. Hallucinations rarely make up invalid grammar or invent non-existent words, what we're concerned about is facts, which isn't relevant at all when the goal is language preservation.
Depending on how powerful the language modeling is, I suspect it could lead to an LLM which will confidently and convincingly tell you how to say things like "floppy disk" and "SSD" in every extinct language and even those that went extinct long before computers ever existed, which is... interesting, but not exactly truth. I've seen LLMs hallucinate nonexistent things in programming languages. It's hard to believe it…
So far I have not had that much luck getting the models to learn the Kiksht grammar and morphology via in-context learning, I think the model will have to be trained on the corpus to actually work for it. I think this mostly makes sense, since they have functionally nothing in common with western languages.
To illustrate the point a bit: the bulk of training data is still English, and in English, the semantics of a sentence are mainly derived from the specific order in which the words appear, mostly because it lost its cases some centuries ago. Its morphology is mainly "derivational" and mainly suffixal, meaning that words can be arbitrarily complicated by adding suffixes to them. So baked into English is word order that sometimes we insert words into sentences simply to make the word order sensible. e.g., when we say "it's raining outside", the "it's" refers to nothing at all—it is there entirely because the word order of English demands that it exists.
Kiksht in contrast is completely different. Its semantics are nearly entirely derived from triple-prefixal structure of (in particular) verbs. Word ordering almost does not matter. There are, like, 12 tenses, and some of them require both a prefix and a reflective suffix. Verbs are often 1 or 2 characters, and with the prefix structure, a single verb can often be a complete sentence. And so on.
I will continue working on this because I think it will eventually be of help. But right now the deep learning that has been most helpful to me has been to do things like computational typology. For example, discovering the "vowel inventory" of a language is shockingly hard. Languages have somewhat consistent consonants, but discovering all the varieties of `a` that one can say in a language is very hard, and deep learning is strangely good at it.
I wonder how useful this really is. No doubt, it's excellent for archiving, but that's not the same as "preserving" culture. If it's not alive and kicking it's not a culture IMO. You see this happen even with texts : once things start being written down, the actual knowledge tends to get lost (see India for example). This "AI to help low-resource languages" thing is a big deal in India too, but it just feels like ano…
That’s surprising and seems different than what I’ve seen for other languages in other parts of the world (even if it’s a relatively new phenomenon).
I am one of these people! I am one of a handful of people who speak my ancestral language, Kiksht. I am lucky to be uniquely well-suited to this work, as I am (as far as I know) the lone person from my tribe whose academic research background is in linguistics, NLP, and ML. (We have, e.g. , linguists, but very few computational linguists.) So far I have not had that much luck getting the models to learn the Kiksht gr…
I am one of these people! I am one of a handful of people who speak my ancestral language, Kiksht. I am lucky to be uniquely well-suited to this work, as I am (as far as I know) the lone person from my tribe whose academic research background is in linguistics, NLP, and ML. (We have, e.g. , linguists, but very few computational linguists.) So far I have not had that much luck getting the models to learn the Kiksht gr…
I am one of these people! I am one of a handful of people who speak my ancestral language, Kiksht. I am lucky to be uniquely well-suited to this work, as I am (as far as I know) the lone person from my tribe whose academic research background is in linguistics, NLP, and ML. (We have, e.g. , linguists, but very few computational linguists.) So far I have not had that much luck getting the models to learn the Kiksht gr…
Earlier quoted context omitted.
This take us overly cynical in this case—the original and primary use case for LLMs is to model languages in a comprehensive way. It's literally in the name. Hallucinations rarely make up invalid grammar or invent non-existent words, what we're concerned about is facts, which isn't relevant at all when the goal is language preservation.
Depending on how powerful the language modeling is, I suspect it could lead to an LLM which will confidently and convincingly tell you how to say things like "floppy disk" and "SSD" in every extinct language and even those that went extinct long before computers ever existed, which is... interesting, but not exactly truth. I've seen LLMs hallucinate nonexistent things in programming languages. It's hard to believe it…
If a language model were asked what the word for "floppy disk" was in an extinct language and it invented a decent circumlocution, I don't think that would be a bad thing. People who are just engaging with the model as a way of connecting with their cultural heritage won't mind much if there is some creative application, and scholars are going to be aware of the limitations.
Again, the misapplication of language models as databases is why hallucinations are a problem. This use case isn't treating the model as a database, it's treating the model as a model, so the consequences for hallucination are much smaller to the point of irrelevance.