The r's in strawberry presents a different level of task to what people imagine. It seems trivial to a naive observer because the answer is easily derivable from the question without extra knowledge.

A more accurate analogy for humans would be to imagine if every word had a colour. You are told that there are also a sequence of different colours that correspond to the same colour as that word. You are even given a book showing every combination to memorise.

You learn the colours well enough that you can read and write coherently using them.

Then comes the question of how many chocolate-browns are in teal-with-a-hint-of-red. You know that teal-with-a-hint-of-red is a fruit and you know that the colour can also be constructed by crimson followed by Disney-blond. Now, do both of those contain chocolate-brown or just one of them, how many?

It requires excersizing memory to do a task that is underrepresented in the training data because humans simply do not have to do the task at all when the answer can be derived from the question representation. Humans also don't have the ability that the LLMs need but the letter representation doesn't need that ability.

I do think this is a tool issue. Here is what the article says:

> For the multiplication task, note that agents that make external calls to a calculator tool may have ZEH = ∞. While ZEH = ∞ does have meaning, in this paper we primarily evaluate the LLM itself without external tool calls

The models can count to infinity if you give them access to tools. The production models do this.

Not that the paper is wrong, it is still interesting to measure the core neural network of a model. But modern models use tools.

Seems like it’s maybe also a tool steering problem. These models should be reaching for tools to help solve factual problems. LLM should stick to prose.