A Knockout Blow for LLMs?

a year ago

Apple's new paper highlights significant weaknesses in LLMs, particularly their inability to generalize beyond their training data.
The paper critiques 'chain of thought' and 'reasoning models,' showing they often fail to produce correct answers even when their reasoning traces appear correct.
LLMs struggle with classic problems like the Tower of Hanoi, performing poorly even when given the solution algorithm.
The paper argues that LLMs cannot reliably solve problems that humans and conventional algorithms handle easily, casting doubt on their potential to achieve AGI.
A weakness in the paper's argument is that humans also have limits, but AGI should combine human adaptability with computational reliability.
LLMs are not a substitute for well-specified conventional algorithms and should not be expected to work reliably in complex scenarios.
The paper suggests that LLMs will continue to have uses in coding, brainstorming, and writing but are not a direct route to transformative AGI.
The field of neural networks and deep learning is not dead, but LLMs have clear limits, and other approaches may thrive.
The paper is seen as an elegant scientific research piece, critiquing the over-reliance on scaling LLMs without theoretical foundations.
True advancement in human civilization requires theory-driven system building, not just 'dumb scaling' of LLMs.

Hasty Briefsbeta