4 ms·

RAG seems to be all the rage. Not to mention the quest for the cooking up the correct cocktail of smaller MoE/ensemble models, and ... there's decades' worth of

by pas 2y ago

RAG seems to be all the rage. Not to mention the quest for the cooking up the correct cocktail of smaller MoE/ensemble models, and ... there's decades' worth of optimization work ahead (a few years of it seems to be already VC and edu grants funded), no?

cs702 2y ago
I think the grandparent comment is about AI research, driven by the quest for AGI. Incremental improvement of proven approaches, driven by profit motive, will surely continue regardless of whether there is an AI winter or not.
- pas 2y ago
  Indeed, but there's probably a very real funding and attention (heh) issue, if there's ton of both then there will be progress. But. Usually the bigger the hype the more progress is expected so the faster the fund-tention will dissipate.
nkozyra 2y ago
RAG takes the current limit of LLM and focuses it on specific problems using custom data. It's not exactly magic, it just finds a way to produce something tangible and usable from an otherwise broadly focused model. It's the rage because it's a way to practically _do work_ from LLMs that generally provide wow from conversationally accurate, often factually accurate responses.
PheonixPharts 2y ago
There's tons of optimization work left, but by it's nature optimization tends to push the limits of what we currently have, and rarely allows for substantial improvements. There are some major limitations to LLMs that aren't going to be "optimized" away. At the end of the day LLMs are Monte Carlo samplers over a latent, compressed representation existing human text data. Many of the tasks people hope LLMs will achieve require major leaps in our current understanding of language modeling. One great example limitation (which shocks me sometimes when I think about it): generating output is still ultimately stuck in looking at the probability of the next token rather than the much more useful, probability of the generated statement. There are techniques to improve this, but we're missing a major piece of generating highly probable statements with no hint about how to really get there. Consider how you might write SQL. You conceive of the high level query first and start sketching out the pieces. An LLM can only look at each token and can't, statistically speaking, think in terms of the entire query. Personally I think LLMs are very underutilized/exploited for what they are good at, and there is way too much focus on what they can't do. Hopefully we'll dodge an AI winter by using LLMs to solve the wide range of classical NLP problems that make many tasks that just a few years ago nearly impossible, rather simple today. Unfortunately the irrational hype around these models makes me skeptical of that scenario.
- mjburgess 2y ago
  The issue with predicting the next statement is the combinatorial infinity which is improbable to model with historical frequencies, ie., P(mat|the cat sat on the) is a distribution over, say, 100k words. Whereas, P(the cat sat|on the mat) is a distribution over 100k^3 words. Part of the illusion of an LLM is that we produce create text in such a highly regular way that a mere distribution over 100k iterated, say 500 times, gives you a page of text as if modelling 100k^500.
- pas 2y ago
  > An LLM can only look at each token and can't, statistically speaking, think in terms of the entire query. There's always some space in high-dimensions to put that extra context somewhere. So yes, only one token is predicted, but there's a lot of information squeezed into the "condition" (as in conditional probability, as mjburgess' comment shows) > [hype instead of NLP] yes, exactly. we'll see what remains after the bubble bursts.

by pas 2y ago

cs702 2y ago

I think the grandparent comment is about AI research, driven by the quest for AGI. Incremental improvement of proven approaches, driven by profit motive, will surely continue regardless of whether there is an AI winter or not.

pas 2y ago
Indeed, but there's probably a very real funding and attention (heh) issue, if there's ton of both then there will be progress. But. Usually the bigger the hype the more progress is expected so the faster the fund-tention will dissipate.

nkozyra 2y ago

RAG takes the current limit of LLM and focuses it on specific problems using custom data. It's not exactly magic, it just finds a way to produce something tangible and usable from an otherwise broadly focused model. It's the rage because it's a way to practically _do work_ from LLMs that generally provide wow from conversationally accurate, often factually accurate responses.

PheonixPharts 2y ago

There's tons of optimization work left, but by it's nature optimization tends to push the limits of what we currently have, and rarely allows for substantial improvements. There are some major limitations to LLMs that aren't going to be "optimized" away. At the end of the day LLMs are Monte Carlo samplers over a latent, compressed representation existing human text data. Many of the tasks people hope LLMs will achieve require major leaps in our current understanding of language modeling. One great example limitation (which shocks me sometimes when I think about it): generating output is still ultimately stuck in looking at the probability of the next token rather than the much more useful, probability of the generated statement. There are techniques to improve this, but we're missing a major piece of generating highly probable statements with no hint about how to really get there. Consider how you might write SQL. You conceive of the high level query first and start sketching out the pieces. An LLM can only look at each token and can't, statistically speaking, think in terms of the entire query. Personally I think LLMs are very underutilized/exploited for what they are good at, and there is way too much focus on what they can't do. Hopefully we'll dodge an AI winter by using LLMs to solve the wide range of classical NLP problems that make many tasks that just a few years ago nearly impossible, rather simple today. Unfortunately the irrational hype around these models makes me skeptical of that scenario.

mjburgess 2y ago
The issue with predicting the next statement is the combinatorial infinity which is improbable to model with historical frequencies, ie., P(mat|the cat sat on the) is a distribution over, say, 100k words. Whereas, P(the cat sat|on the mat) is a distribution over 100k^3 words. Part of the illusion of an LLM is that we produce create text in such a highly regular way that a mere distribution over 100k iterated, say 500 times, gives you a page of text as if modelling 100k^500.
pas 2y ago
> An LLM can only look at each token and can't, statistically speaking, think in terms of the entire query. There's always some space in high-dimensions to put that extra context somewhere. So yes, only one token is predicted, but there's a lot of information squeezed into the "condition" (as in conditional probability, as mjburgess' comment shows) > [hype instead of NLP] yes, exactly. we'll see what remains after the bubble bursts.