I recently went to the LLM chat arena and tried my "test input" against the latest frontier models that GPT 3 failed on. This test snippet simply repeats the same four-letter word in a paragraph many times using all of its various possible meanings simultaneously. The request to the AI is to put the meaning of each usage of the word next to it in brackets.

None of the frontier models can do this perfectly. They all screw up to various degrees in various interesting ways. A schoolkid could do this flawlessly.

This is not some contrived test with bizarre picture puzzles as seen in ARC-AGI or testing obscure knowledge about bleeding-edge scientific research. It's simple English comprehension using a word my toddler knows already!

It does reveal the fundamental flaw in all transformer-based models: They're just shifting vectors around with matrices, and are unable to deal with many categories of inputs that cause overlaps or bring too many of the tokens too close to each other in some internal representation. They get muddled up and confused, resulting in errors in the output.

I see similar effects when using LLMs for programming: They get confused when there are many usages of the same identifier or keyword, but with some subtle difference such as being inside a comment, string, or in a local context where the meaning is different.

I suspect this will be eventually fixed, but I haven't seen any fundamental improvement in three years.