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Comment by Herval_freire | Hacker News Reader

Someone in a previous comment on LLM research said that according to what he knew about LLM research that we were in a local maximum that no further improvement was likely possible.

I disagreed with him, and this article is evidence that is in favor of my point. If research like this continues to move forward LLMs will improve at a rapid rate.

Different threads attract different groups of people with different areas of expertise. So I will sort of reiterate this topic here as I'm interested. What are most people's thoughts on this "local maximum" thing have we actually hit a dead end? Especially given the proliferation of effort towards producing research like the one showed in the topic here.

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Once you exhaust the dataset of all written language, the next step is multi-modal — images, audio, video. What will next-token prediction give us on such a dataset? Better versions of what we have now — style transfer, summarization, captioning, translation, prompt-based generation, etc., but with synesthesia.

There is still plenty of improvement ahead but I don’t think anything genuinely surprising will come from the current regime of feedforward models. What is missing is action — an interactive feedback loop between agent and environment. Progress in RL and robotics has been very slow by comparison and unless we see a breakthrough there, I would guess the GPT phase plateaus in the next 5-10 years.

> What will next-token prediction give us on such a dataset?

Haven’t we pretty much figured out these things are doing more than just predicting the next token at this point?

There’s probably a lot to be done with a “prediction machine”, birds aren’t all that smart but can catch bugs in midair.

People keep underestimating what next-token prediction can do, but they’re not wrong that it’s how LLM’s work.

It’s actually a good question: what will next-token prediction be able to do on new datasets? The error is thinking you can answer it, even in broad terms.

I expect progress will be much less predictable. Some kinds of action look pretty easy; it depends on the domain.

For example, I expect skill at writing some kinds of code to improve dramatically because running tests in a sandbox looks easy. It’s already being researched. [1] Extending that to device drivers might be a bit harder. Fuzzing is already mostly automated and smarter fuzzing could get pretty scary.

[1] https://nanothoughts.substack.com/p/reflecting-on-reflexion

Basically, if the knowledge exists online in a way that can be pieced together in a straight forward manner, GPT will figure it out, but for information that requires experimentation and creating new information to derive results, it won't be of much use. For example, GPT can't iteratively try different programming techniques to speed up a block of parallelizable code, it'll simply give you the best guess that it can find off google.

It won't iterate on its own, but you can do it. You can ask it for a list of things to try, and they will be different alternatives. You can also tell it the result of an experiment and it will often figure out what to fix.

If you follow the link I shared, some researchers automated asking GPT4 to write tests, running the tests in a sandbox, and feeding the results back in.

Why you assume it won't?

With access to python interpreter I don't see any issue

Yea, after all these LLMs are predicting one sequence of tokens from another sequence of tokens and the tokens could be anything, it just "happens" that text has the most knowledge and the easiest to input, then there are image, sound, video, but tokens could also be learned from world experience in RL:

Transformers are Sample-Efficient World Models:

https://github.com/eloialonso/iris#transformers-are-sample-e...

I mean I just read that article and it doesn't seem like a lot will change, sure it can summarize a whole book, or read a larger chunk of code to do thing with, but didn't really see it talk about taking things to "the next level" so to speak.

The researchers are also, excited.

We’re especially motivated by applications that could benefit from longer-sequence models – high-resolution imaging, new modalities of data, language models that can read entire books. Imagine giving a language model an entire book and having it summarize the plot, or conditioning a code generation model on all the code you’ve ever written. The possibilities are wild – and we’re excited.

But this research came out mere weeks after the release of gpt-4. That is in itself rapid. If small incremental changes like this continue on a sort of monthly basis, the trendline points towards something that's not a dead end. That's my view of it.

As with most technology there isn't necessarily always a constant influx of inflection points and paradigm shifts. Improvement will likely creep up on us incrementally. Suddenly one day its clearly more intelligent then a human and we can't point to when it happened.

GPT-4's release doesn't seem like the relevant time marker, since nothing in the article builds on it or depends on it. The paper for H3 was submitted in December 2022.

The pace the last few years definitely seems rapid, just don't want there to be a false impression.

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