[BiasRegularizer]

It's not impossible to see that LLMs can make finite step "reasoning" between input and output, as each block of the transformer can model probabilistic causality. Transformers are sometimes considered a fully connected graph neural network, which can be used for modeling causal graphs. One additional supporting evidence on the finite step reasoning hypothesis is that "train of thought" improves the performance of LLMs[1], meaning that letting the model explain itself explicitly reduces the amount of implicit "reasoning" steps that need to happen within the model.

Additionally, while we don't know how us humans functionally reason, it's believed that the predictive nature of our brain is central to our reasoning abilities. Maybe in some way, the autoregressive nature of LLMs is similar to our predictive brain.

For a collection of emergent properties in LLMs, I recommend this paper [2]

[1]https://arxiv.org/abs/2201.11903.pdf [2]https://arxiv.org/pdf/2206.07682.pdf

[tomrod]

Thanks for the references, will review.