Ok, so, this is my impression from shoving philosophy texts into it.
For things that already have a large body of scholarship, and have a set of fairly solidified interpretations, it is very good at giving summaries. But for works that still remain enigmatic and difficult to interpret, it fails to produce anything new or interesting.
It seems to be a more complex version of ChatGPT, but it has the same underlying problems, so its not useful for someone doing academic work or trying to create something radically new, as with other LLMs in the past.
I thought it goes without saying that these types of things depend on existing data. It seems useful to learn about something on your walk or commute but not take your research to the next level.
I'm just saying that, just like other models, it appears at first to have a great deal of use value but in actuality it only works for small edge cases and on things that you can find easily yourself without the model.
For things that already have a large body of scholarship, and have a set of fairly solidified interpretations, it is very good at giving summaries. But for works that still remain enigmatic and difficult to interpret, it fails to produce anything new or interesting.
It seems to be a more complex version of ChatGPT, but it has the same underlying problems, so its not useful for someone doing academic work or trying to create something radically new, as with other LLMs in the past.