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Comment by fykem | Hacker News Reader

More processing power does not make a model better. You can train models on CPUs with same result based on same model architecture and dataset. It'll just take longer to get those results.

What makes models "good" is if the dataset "fits" the model architecture properly and you have given it enough time (epochs) to have a semi accurate prediction ratio (lets say 90% accurate). For image classification models I've done around ~100 epochs for 10,000 items seems to be the best certain data sets will ever get. There will at some point come a time when the continued training of the model is either underfitting/overfitting and no amount of continued training/processing power would help improve it.

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The OP asks "per request", not training time.

Answer is still no and still for the above reason. Compute resources are only relevant to how fast it can answer not the quality.

Then why does chain of thought work better than asking for short answers?

Because it’s a better prompt. Works better for people too.

That's not the only reason.

More tokens = more useful compute towards making a prediction. A query with more tokens before the question is literally giving the LLM more "thinking time"

It’s not clear that more tokens are better.

I think it's pretty clear

https://arxiv.org/abs/2310.02226

I mean, i can imagine you wouldn't always need the extra compute.

This paper is a great illustration of how little is understood about this question. They discovered that appending dummy tokens (ignored during both training and inference) improves performance somehow. Don’t confuse their guess as to why this might be happening with actual understanding. But in any case, this phenomenon has little to do with increasing the size of the prompt using meaningful tokens. We still have no clue if it helps or not.

I just found this paper i read a while ago. Doesn't this answer the question ?

The Impact of Reasoning Step Length on Large Language Models - https://arxiv.org/abs/2401.04925

>They discovered that appending dummy tokens (ignored during both training and inference) improves performance somehow. Don’t confuse their guess as to why this might be happening with actual understanding.

More tokens is more compute time for the model to utilize, that is completely true.

What they guess is that the model can utilize the extra compute for better predictions even if there's no extra information to accompany this extra "thinking time".

Yes, more tokens means doing more compute, that much is true. The question is whether this extra compute helps or hurts. This question is yet to be answered, as far as I know. I tend to make my GPT-4 questions quite verbose, hoping it helps.

This is completely orthogonal to CoT, which is simply a better prompt - it probably causes some sort of better pattern matching (again very poorly understood).

>The question is whether this extra compute helps or hurts.

I've linked 2 papers now that show very clearly the extra compute helps. I honestly don't understand what else it is you're looking for.

>This is completely orthogonal to CoT, which is simply a better prompt - it probably causes some sort of better pattern matching (again very poorly understood).

That paper specifically dives in on the effect of the length of the CoT prompt. It makes little sense to say - "oh it's just the better prompt" when Cot prompts with more tokens perform better than the shorter ones even when the shorter ones contain the same information. There is also the clear correlation with task difficulty and length.

Yes, the CoT paper does provide some evidence that a more verbose prompt works better. Thank you for pointing me to it.

Though I still don’t quite understand what is going on in the dummy tokens paper - what is “computation width” and why would it provide any benefit?

So "compute" includes just having more data ... that can also be "ignored"/ "skipped" for whatever reasons (e.g. weights), ok.

I have a theory that the results are actually a side effect of having the information in a different area of the context block.

Models can be sensitive to the location of a needle in the haystack of its input block.

It's why there are models which are great at single turn conversation but can't hold a conversation past that without multi-turn training.

You can even corrupt the outputs by pushing past the number of turns / show the model data in a form it hasn't really seen before.

Models can be sensitive to the location of a needle in the haystack of its input block.

But only if we use some sort of attention optimization. For the quadratic attention algo it shouldn’t matter where the needle is, right?

It correlates but the intuition is a bit misleading. What's actually happening is that by asking a model to generate more tokens, it increases the amount of information it has learnt to be present in its context block.

It's why "RAG" techniques work, the models learn during training to make use of information in context.

At the core of self-attention is dot product measurement which causes the model to act like a search engine.

It's helpful to think about it in terms of search: the shape of the outputs look like conversation but were actually prompting the model to surface information from the QKV matrices internally.

Does it feel familiar? When we brainstorm we usually chart graphs of related concepts e.g. blueberry -> pie -> apple.

>What's actually happening is that by asking a model to generate more tokens, it increases the amount of information it has learnt to be present in its context block.

I'm not saying this isn't part of it but even if it's just dummy tokens without any new information, it works.

https://arxiv.org/abs/2310.02226

Ok, thanks. My misconception kind of prohibited the insight of a potential (theoretical) assert statement, which is kind of what is meant by

> if the [resulting] dataset "fits" the model architecture properly,

right?

I have too many questions. It seems unreasonable to ask away and I should instead read the studies and some books.

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