Comment by vita7777777
16 days ago
This is very thoughtful and interesting. It's worth noting that this is just a start and in future iterations they're planning to give the LLMs much more to work with (e.g. news feeds). It's somewhat predictable that LLMs did poorly with quantitative data only (prices) but I'm very curious to see how they perform once they can read the news and Twitter sentiment.
I would argue that sentiment classification is where LLMs perform best. folks are already using it for precisely such purpose - have even built a public index out of it
what index ?
found one such index https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5763042 called Populism Index (POP) built from Wall Street Journal articles (not sure how publicly accessible it is)
sorry dude. tried going down the rabbit hole but I'm too lazy and uninterested in it. read about it month ago or so. perhaps Daily News Sentiment Index uses LLMs, not sure. if you go long enough through https://quantocracy.com/ you should be able to find it
Not just can i guarantee the models are bad with numbers, unless it's a highly tuned and modified version they're too slow for this arena. Stick to using attention transformers in better model designs which have much lower latencies than pre-trained llms...