[SpicyLemonZest]

> But LLMs fail the most basic tests of understanding that don't require complexity.

Which basic tests of understanding do state-of-the-art LLMs fail? Perhaps there's something I don't know here, but in my experience they seem to have basic understanding, and I routinely see people claim LLMs can't do things they can in fact do.

[13years]

Take a look at this vision test - https://www.mindprison.cc/i/143785200/the-impossible-llm-vis...

It is an example that shows the difference between understanding and patterns. No model actually understands the most fundamental concept of length.

LLMs can seem to do almost anything for which there are sufficient patterns to train on. However, there aren't infinite patterns available to train on. So, edge cases are everywhere. Such as this one.

[SpicyLemonZest]

I don't see how this shows that models don't understand the concept of length. As you say, it's a vision test, and the author describes how he had to adversarially construct it to "move slightly outside the training patterns" before LLMs failed. Doesn't it just show that LLMs are more susceptible to optical illusions than humans? (Not terribly surprising that a language model would have subpar vision.)

[13years]

But it is not an illusion, and the answers make no sense. In some cases the models pick exactly the opposite answer. No human would do this.

Yes, outside the training patterns is the point. I have no doubt if you trained LLMs on this type of pattern with millions of examples it could get the answers reliably.

The whole point is that humans do not need data training. They understand such concepts from one example.