pal VS qagnn

Using the terminology that I'm working with this is an example of a second-order analytic augmentation!

Here's another approach of second-order analytic augmentation, PAL: https://reasonwithpal.com

And third-order, Toolformer: https://arxiv.org/abs/2302.04761

The difference isn't in what is going on but rather with framing the approach within the analytic-synthetic distinction developed by Kant and the analytic philosophers who were influenced by his work. There's a dash of functional programming thrown in for good measure!

I have scribbled on a print-out of the article on my desk:

  Nth Order

GitHub: https://github.com/reasoning-machines/pal

tl;dr -- LLMs are bad at basic arithmetic and logic (as their opening examples with math word problems show), but they do much better if instead of asking them for the answer, you ask for code to compute the answer. Then evaluate or run the code to get the answer.

QA-GNN (https://github.com/michiyasunaga/qagnn from a Stanford lab) had some issues with their evaluation, but more importantly this work 'GNN is counting?...' (https://openreview.net/forum?id=hzmQ4wOnSb) showed that they can achieve better results with an extremely simplistic 1-dim GNN model - so the performance of QA-GNN was mainly due to data. AFAIK there were discussions around this, but now if you go to QA-GNN repo they have disabled issues tab.