[imtringued]

>To overcome this difficulty, DeepMind paired a language model with a more traditional symbolic deduction engine that performs algebraic and geometric reasoning.

I couldn't think of a better way to demonstrate that LLMs are poor at reasoning than using this crutch.

[FrustratedMonky]

I wouldn't say 'crutch' but component.

Eventually LLMs will be plugged into Vision Systems, and Symbolic Systems, and Motion Systems, etc... etc...

The LLM wont be the main 'thing'. But the text interface.

Even human brain is bit segmented with different faculties being 'processed' in different areas with different architectures.

[fennecfoxy]

I suppose it's because LLM training data uses text that can contain reasoning within it, but without any specific context to specifically learn reasoning. I feel like the little reasoning an LLM can do is a byproduct of the training data.

Does seem more realistic to train something not on text but on actual reasoning/logic concepts and use that along with other models for something more general purpose. LLMs should really only be used to turn "thoughts" into text and to receive instructions, not to do the actual reasoning.