[ajuc]

It's not thinking, it compressed the internet into a clever, lossy format with nice interface and it retrieves stuff from there.

Chain of thought is like trying to improve JPG quality by re-compressing it several times. If it's not there it's not there.

[Jerrrrrrry]

  >It's not thinking



  >it compressed the internet into a clever, lossy format with nice interface and it retrieves stuff from there.

Humans do both, why can't LLM's?

  >Chain of thought is like trying to improve JPG quality by re-compressing it several times. If it's not there it's not there.

More like pulling out a deep-fried meme, looking for context, then searching google images until you find the most "original" JPG representation with the least amount of artifacts.

There is more data to add confidently, it just has to re-think about it with a renewed perspective, and an abstracted-away higher-level context/attention mechanism.

[danenania]

> Chain of thought is like trying to improve JPG quality by re-compressing it several times. If it's not there it's not there.

Empirically speaking, I have a set of evals with an objective pass/fail result and a prompt. I'm doing codegen, so I'm using syntax linting, tests passing, etc. to determine success. With chain-of-thought included in the prompting, the evals pass at a significantly higher rate. A lot of research has been done demonstrating the same in various domains.

If chain-of-thought can't improve quality, how do you explain the empirical results which appear to contradict you?

[mplewis]

The empirical results like OP’s paper, in which chain of thought reduces quality?

[danenania]

The paper is interesting because CoT has been so widely demonstrated as effective. The point is that it "can" hurt performance on a subset of tasks, not that CoT doesn't work at all.

It's literally in the second line of the abstract: "While CoT has been shown to improve performance across many tasks..."

[easyThrowaway]

I have no idea how accurate it actually is, But I've had the process used by LLM described as the following: "Think of if like a form of UV Mapping, applied to language constructs rather than 3D points in space, and the limitations and approximations you experience are similar to those emerging when having to project a 2D image over a 3D surface."

[Eisenstein]

These kind of reductive thought-terminating cliches are not helpful. You are using a tautology (it doesn't think because it is retrieving data and retrieving data is not thinking) without addressing the why (why does this preclude thinking) or the how (is it doing anything else to generate results).

[lucianbr]

> If it's not there it's not there.

There is nothing in the LLM that would have the capability to create new information by reasoning, when the existing information does not already include what we need.

There is logic and useful thought in the comment, but you choose not to see it because you disagree with the conclusion. That is not useful.

[Eisenstein]

I'm sorry but generating logic from tautologies is not useful. And the conclusion is irrelevant to me. The method is flawed.

[bongodongobob]

Maybe if you bury your head in the sand AI will go away. Good luck!

[lucianbr]

This is basically a reformulation of "have fun staying poor!". Even contains the exclamation mark.

Those people sure showed us, didn't they? Ah, but "it's different this time!".