3 ms·
I know many humans who wouldn't pass this test, e.g. in their non-native language... [ insert humiliating attempts to speak <language> ] Try instead to give GP
by asah 3y ago
I know many humans who wouldn't pass this test, e.g. in their non-native language... [ insert humiliating attempts to speak <language> ]
Try instead to give GPT more examples of pronunciation (1000+) and then I suspect it'll do fine.
- glhaynes 3y agoIsn't this just arguing for giving it more text to do text generation from?
- pixl97 3y agoDepends what you consider vocal models 'more text'. Already we're seeing multi-modal AI have a correct answer when executing across image/text or voice/text interpretation.
- hajile 3y agoIf I had access to a dictionary with pronunciation symbols and a thesaurus, I could do it regardless of language. The LLM not only has access, but has baked both its model. This problem is EXACTLY the kind of problem computers should be good at. The real issue is that LLMs don’t think.
- vidarh 3y agoHumans "have access" too in the languages we speak in the same way the LLM does, and I'd posit that many of us would still fail. It's "baked into us" just like it is into an LLM. I don't see any reason why you'd assume an LLM would be better able to recall this information than a human who will if anything have vastly more training at mapping words to sounds. It's a problem a computer with a custom written algorithm should be good at. Why we'd assume a model trained on just general data will automatically be good at this is a bizarre notion to me. We don't automatically assume humans will be great at everything just because we've passively consumed lots of content. To me, a whole lot of these "LLMs don't think" claims comes from not thinking about what reasoning and thinking is, and whether or not how we try to measure that makes any sense at all.