Comment by bglazer

8 days ago

This is a very tiring criticism. Yes, this is true. But, it's an implementation detail (tokenization) that has very little bearing on the practical utility of these tools. How often are you relying on LLM's to count letters in words?

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bglazer

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1970-01-01  8 days ago

The implementation detail is that we keep finding them! After this, it couldn't locate a seahorse emoji without freaking out. At some point we need to have a test: there are two drinks before you. One is water, the other is whatever the LLM thought you might like to drink after it completed refactoring the codebase. Choose wisely.

101008  8 days ago

It's an example that shows that if these models aren't trained in a specific problem, they may have a hard time solving it for you.

  • altruios  8 days ago

    An analogy is asking someone who is colorblind how many colors are on a sheet of paper. What you are probing isn't reasoning, it's perception. If you can't see the input, you can't reason about the input.

    • 9rx  8 days ago

      > What you are probing isn't reasoning, it's perception.

      Its both. A colorblind person will admit their shortcomings and, if compelled to be helpful like an LLM is, will reason their way to finding a solution that works around their limitations.

      But as LLMs lack a way to reason, you get nonsense instead.

      2 replies →

  • Uehreka  8 days ago

    No, it’s an example that shows that LLMs still use a tokenizer, which is not an impediment for almost any task (even many where you would expect it to be, like searching a codebase for variants of a variable name in different cases).

iAMkenough  8 days ago

The criticism would stop if the implementation issue was fixed.

It's an example of a simple task. How often are you relying on LLMs to complete simple tasks?