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So (if I get it right) you are using the LLM to convert the human language question into (presumably) code that is basis for running "normal" searches and retur

So (if I get it right) you are using the LLM to convert the human language question into (presumably) code that is basis for running "normal" searches and returning text. Avoiding any fears of hallucinations. But earlier you say "LLMs will get better at comprehension". So are you using an LLM to markup the original text in some way ?

nnechm 3y ago
Yes, that's right, I avoid hallucinations that way. In addition, I mark up existing texts so that the LLM knows what it is reading even if it is a piece of a larger text, so for e.g if you need to get Apple's Greater China sales for each quarter in 2020... https://imgur.com/a/oCCay3o https://imgur.com/a/oCCay3o I am not making 4 calls with the entire text, I instead get pieces of each which would best match the question. There are additional challenges, for instance, GPT4 struggles to know the difference between the words guidance and outlook, which mean the same thing but somehow they don't for GPT4. When I say they become better readers, I meant it in a general sense as in better in the case above. You basically have someone who can read through tables really well, and that can change investment research fundamentally, which is a lot of reading tables and graphs :)