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In the limit case, to an actual general intelligence, representation is superfluous, because it can figure out how to convert freely.

To the extent that the current generation of AI isn't general, yeah, papering over some of its weaknesses may allow you to expose other parts of it, both strengths and other weaknesses.

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A human can easily struggle at solving a poorly communicated puzzle, especially if paper/pencil or something isn't available to convert to a better format. LLMs can look back at what they wrote, but it seems kind of like a poor format for working out a better representation to me.

I found some papers [n] about this. And I think the answer is yes, the format matters asnd hence the representation.

I wonder if the author would be willing to try with another representation.

[1]: Does Prompt Formatting Have Any Impact on LLM Performance? https://arxiv.org/html/2411.10541v1

[2]: Large Language Models(LLMs) on Tabular Data: Prediction, Generation, and Understanding - A Survey https://arxiv.org/html/2402.17944v2

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