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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 im

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.

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.