Comment by apercu

While I may be mistaken, but I don't believe that LLMs are trained on a large corpus of machine readable music representations, which would arguably be crucial to strong performance in common practice music theory. I would also surmise that most music theory related datasets largely arrive without musical representations altogether. A similar problem exists for many other fields, particularly mathematics, but it is much more profitable to invest the effort to span such representation gaps for them. I would not gauge LLM generality on music theory performance, when its niche representations are likely unavailable in training and it is widely perceived as having miniscule economic value.

music theory is a really good test because in my experience the AI is extremely bad at it

> In my experience LLMs can't get basic western music theory right, there's no way I would use an LLM for something harder than that.

This take is completely oblivious, and frankly sounds like a desperate jab. There are a myriad of activities whose core requirement is a) derive info from a complex context which happens to be supported by a deep and plentiful corpus, b) employ glorified template and rule engines.

LLMs excel at what might be described as interpolating context following input and output in natural language. As in a chatbot that is extensivey trained in domain-specific tasks, which can also parse and generate content. There is absolutely zero lines of intellectual work that do not benefit extensively from this sort of tool. Zero.