But here’s the really funky bit. If you ask Claude how it got the correct answer of 95, it will apparently tell you, “I added the ones (6+9=15), carried the 1, then added the 10s (3+5+1=9), resulting in 95.” But that actually only reflects common answers in its training data as to how the sum might be completed, as opposed to what it actually did.
This is not surprising. LLMs are not designed to have any introspection capabilities.
Introspection could probably be tacked onto existing architectures in a few different ways, but as far as I know nobody’s done it yet. It will be interesting to see how that might change LLM behavior.
I’m surprised that they are surprised by this as well. What did they expect, and why? How much of this is written to imply LLMs - their business - are more advanced/capable than they actually are?
This is not surprising. LLMs are not designed to have any introspection capabilities.
Introspection could probably be tacked onto existing architectures in a few different ways, but as far as I know nobody’s done it yet. It will be interesting to see how that might change LLM behavior.
I’m surprised that they are surprised by this as well. What did they expect, and why? How much of this is written to imply LLMs - their business - are more advanced/capable than they actually are?
Then take that concept further, and let it keep introspecting and inspecting how it comes to the conclusions it does and eventually…