Ask HN: Why is it taken for granted that LLM models will keep improving?

I'm curious if limits of like, thermodynamics, won't play a part here. Or maybe also ecological limits: how long will we allow corporations to use essential, scarce resources to train models without paying their fair share? [0]

I'm not an expert here either but I wonder if there will be the same "leap" we saw from ChatGPT3-4 or if there's a diminishing curve to performance, ie: adding another trillion parameters has less of a noticeable effect than the first few hundred billion.

[0] https://fortune.com/2023/09/09/ai-chatgpt-usage-fuels-spike-... -- I am fairly certain they paid for that water, it was not a commensurate price given the circumstances, and if they had to ask to use it first the answer would have been, no, by a reasonable environmental stewardship organization.

What's not mentioned here is test-time compute. Idea being that, sure, you can spend a ton of compute power on pre-training and fine-tuning, but generation is difficult. So instead of spending all time and power more focused on that, how about spending some time and power on it for the model to generate a bunch of possibilities, and then spend the rest of time having a model verify what's been generated for correctness. That's the Let's Verify Step by step.

Great video to talk about this: https://www.youtube.com/watch?v=ARf0WyFau0A

In threads on LLMs, this point doesn't get brought up as much as I'd expect, so I'm curious if I'm missing talks on this or maybe it's wrong. But I see this as the way forward. Models generating tons of answers, and other models being able to pick out the correct ones, and the combinations being beyond human ability, where after, humans can do their own verification.

Edit:

Think of it this way. Trying to create something isn't easy. If I was to write a short story, it'd be very difficult, even if I spent years reading what others have written to learn their patterns. If I then tried to write and publish a single one myself, no chance it'd be any good.

But _judging_ short stories is much easier to do. So if I said screw it, I'll read a couple stories to get the initial framework, then write 100 stories in the same amount of time I'd have spent reading and learning more about short stories, I can then go through the 100 and pick out the one I think is the best and publish that.

That's where I see LLMs going and what the video and papers mentioned in the video say.

I don't think it's a universal assumption. Some people do think it will hit a wall (and maybe do so soon), others think it can keep improving easily by scaling up the compute or the training data.

Good LLMs like ChatGPT are a relatively new technology so I think it's hard to say either way. There might be big unrealized gains by just adding more compute, or adding/improving training data. There might be other gains in implementation, like some kind of self-improvement training, a better training algorithm, a different kind of neural net, etc. I think it's not unreasonable to believe there are unrealized improvements given the newness of the technology.

On the other hand, there might be limitations to the approach. We might never be able to solve for frequent hallucinations, and we might not find much more good training data as things get polluted by LLM output. Data could even end up being further restricted by new laws meaning this is about the best version we will have and future versions will have worse input data. LLMs might not have as many "emergent" behaviors as we thought and may be more reliant on past training data than previously understood, meaning they struggle to synthesize new ideas (but do well at existing problems they've trained on). I think it's also not unreasonable to believe LLMs can't just improve infinitely to AGI without more significant developments.

Speculation is always just speculation, not a guarantee. We can sometimes extrapolate from what we've seen, but sometimes we haven't seen enough to know the long term trend.

They don't. They say things like "infinite resources for everyone" and "compute/electricity too cheap to meter" and so on and so forth. I have distilled the techno-optimist manifesto down to what it actually looks like in reality, i.e. a global panopticon that controls everything with algorithms.

Per usual, I can build this technological panopticon/utopia for a bargain price of $80B. Some people think it can be done for cheaper but they haven't spent as much time as I have on this problem. I have the architecture ready to go, all I need is the GPUs, cameras, microphones, speakers, and wireless data network. The software is the easy part but the panoptic infrastructure is what requires the most capital. The software/brain can be done for maybe $2B but it needs eyes, ears, and a mouth to actually be useful.

The second stage is building up the actuators to bypass people but once the panopticon is ready it won't be hard to build up the robot factories to enact the will of AGI directly via robots acting on the environment.

The recent history of bigger LLMs suddenly being capable of new things is kind of miraculous. This blog post is a decent overview: https://blog.research.google/2022/11/characterizing-emergent... " In many cases, the performance of a large language model can be predicted by extrapolating the performance trend of smaller models."