visarga t1_j202im8 wrote on December 28, 2022 at 6:03 PM

> Co-founder of Neeva

Ok, so direct competition for search is commenting on Google. Maybe they want to imply they also have a language model that is special and closed, and worthy of receiving investments.

I don't believe what he says, there are no signs of that happening. On the contrary, it would seem the head of the pack is just 6-12 months ahead. Everything trickles down pretty quickly. There are still many roadblocks to AGI and no lab is within striking distance.

We already have nice language models, now we need something else - validation systems. So we can use our language models without worrying they would catastrophically hallucinate or miss a trivial thing. We want to keep the useful 90% and drop the bad 10%. It is possible to integrate web search, knowledge bases and python code execution into the model to keep it from messing up. This is what I see ahead, not the end of open research.

footurist t1_j20xxwf wrote on December 28, 2022 at 9:29 PM

I highly doubt this validation route would go nearly as smooth as the path hereto. I mean the very root cause for GPT messing up so often and in such strange ways is that there's no real reasoning there, only surprisingly well working emulation of reasoning.

However, for validation this emulated reasoning won't nearly cut it. So you end up where you started : finding architectures that can actually reason, which of course nobody knows...

If you were thinking about something like trying to match its responses to similar "actual" search results and then validating via comparison to that : What mechanism to use? Because this seems to require actual reasoning aswell.

treedmt t1_j219ah9 wrote on December 28, 2022 at 10:48 PM

Could better, larger datasets be solution to the hallucination problem? Ref chinchilla for example- but maybe even an order of magnitude bigger than that?

visarga t1_j2axzal wrote on December 30, 2022 at 10:31 PM

There are approaches to combine multiple stages of language modelling and retrieval. Demonstrate Search Predict: Composing retrieval and language models for knowledge intensive NLP.

This paper is very interesting. They don't create or fine-tune new models. Instead they create sophisticated pipelines of language models and retrieval models. They even publish a new library and show this way of working with LMs.

Practically, by combining retrieval with language modelling it is possible to verify against references. The ability to freely combine these transformations opens up the path to consistency verification. A LM could check itself for contradictions.