Order Doesn’t Matter, But Reasoning Does
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Re: Order Doesn’t Matter, But Reasoning Does
#2When a language model is trained for chain-of-thought reasoning, particularly on datasets with a limited number of sequence variations, it may end up memorizing predetermined step patterns that seem effective but don’t reflect true logical understanding. Rather than deriving each step logically from the previous ones and the given premises, the model might simply follow a “recipe” it learned from the training data. As a result, this adherence to learned patterns can overshadow genuine logical relationships, causing the model to rely on familiar sequences instead of understanding why one step logically follows from another.
In other words, language models are advanced pattern recognizers that mimic logical reasoning without genuinely understanding the underlying logic.
We might need to shift our focus on the training phase for better performance?
Re: Order Doesn’t Matter, But Reasoning Does
#3When a language model is trained for chain-of-thought reasoning, particularly on datasets with a limited number of sequence variations, it may end up memorizing predetermined step patterns that seem effective but don’t reflect true logical understanding. Rather than deriving each step logically from the previous ones and the given premises, the model might simply follow a “recipe” it learned from the training data. A…
But John Carmack promised me AGI....
Re: Order Doesn’t Matter, But Reasoning Does
#4Is there someone you're trying to disprove? LLMs are inherently statistical, as opposed to other techniques that rely on symbolic or logical relationships. I'm no expert but this is one of the very first things I learned when taking a class on neural networks.
Re: Order Doesn’t Matter, But Reasoning Does
#5Statistical inference is a form of logic. Can it do pure logical deduction? No. And neither can humans without some underlying pattern recognition to form premises. This notion of true "understanding" is a fantasy.
Re: Order Doesn’t Matter, But Reasoning Does
#6When a language model is trained for chain-of-thought reasoning, particularly on datasets with a limited number of sequence variations, it may end up memorizing predetermined step patterns that seem effective but don’t reflect true logical understanding. Rather than deriving each step logically from the previous ones and the given premises, the model might simply follow a “recipe” it learned from the training data. A…
Isn't that what the study you linked to roughly proposes?
Re: Order Doesn’t Matter, But Reasoning Does
#7When a language model is trained for chain-of-thought reasoning, particularly on datasets with a limited number of sequence variations, it may end up memorizing predetermined step patterns that seem effective but don’t reflect true logical understanding. Rather than deriving each step logically from the previous ones and the given premises, the model might simply follow a “recipe” it learned from the training data. A…
> instead of understanding why one step logically follows from another
There’s currently 0% chance of “understanding” happening at any point with this technology.
Re: Order Doesn’t Matter, But Reasoning Does
#8[deleted]
Re: Order Doesn’t Matter, But Reasoning Does
#9When a language model is trained for chain-of-thought reasoning, particularly on datasets with a limited number of sequence variations, it may end up memorizing predetermined step patterns that seem effective but don’t reflect true logical understanding. Rather than deriving each step logically from the previous ones and the given premises, the model might simply follow a “recipe” it learned from the training data. A…
> instead of understanding why one step logically follows from another There’s currently 0% chance of “understanding” happening at any point with this technology.
How do we define understanding?
Re: Order Doesn’t Matter, But Reasoning Does
#10When a language model is trained for chain-of-thought reasoning, particularly on datasets with a limited number of sequence variations, it may end up memorizing predetermined step patterns that seem effective but don’t reflect true logical understanding. Rather than deriving each step logically from the previous ones and the given premises, the model might simply follow a “recipe” it learned from the training data. A…
But John Carmack promised me AGI....
I haven't kept up with his tweets, but I got the impression he deliberately chose to not get involved in LLM hype in his own AI research?