empirical-philosophy VS pal

One way that I've been framing this in my head (and in an application I'm building) is that gpt-3 will be useful for analytic tasks where as gpt-4 will be required for synthetic tasks. I'm using "analytic" and "synthetic" in the same way as in this writeup https://github.com/williamcotton/empirical-philosophy/blob/m...

Hey Simon! I've been digging your writings on LLMs lately.

I've been having some decent luck with some of the approaches that I've discussed in the following articles and projects:

From Prompt Alchemy to Prompt Engineering: An Introduction to Analytic Augmentation: https://github.com/williamcotton/empirical-philosophy/blob/m...

https://www.williamcotton.com/articles/writing-web-applicati...

https://github.com/williamcotton/transynthetical-engine

I'd love to hear your thoughts on the matter!

You’re not actually doing any research.

Here is my research: https://github.com/williamcotton/empirical-philosophy/blob/m...

It is clear that analytic augmentations will result in more factual information.

Your claims are unfounded and untested.

Take a look at

https://github.com/williamcotton/empirical-philosophy/blob/m...

https://langchain.readthedocs.io/en/latest/

They can be taught!

I've been developing a methodology around prompt engineering that I have found very useful:

https://github.com/williamcotton/empirical-philosophy/blob/m...

A few more edits and it's ready for me to submit to HN and then get literally no further attention!

That's not a rebuttable of a claim that Bing is more accurate.

A proper rebuttable would involve empirical evidence that Bing is no more accurate than other LLM tools that do not add analytical augmentations such as search results to their prompts.

Based on empirical evidence, I find that analytical augmentations do indeed result in more accurate results:

https://github.com/williamcotton/empirical-philosophy/blob/m...

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.