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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.
by 101008 10mo 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.
- Uehreka 10mo agoNo, 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).
- 8note 10mo agothe question remains: is the tokenizer going to be a fundamental limit to my task? how do i know ahead of time?
- worldsayshi 10mo agoWould it limit a person getting your instructions in Chinese? Tokenisation pretty much means that the LLM is reading symbols instead of phonemes. This makes me wonder if LLMs works better in Chinese.
- victorbjorklund 10mo agoNo, it is the issue with the tokenizer.
- altruios 10mo agoAn 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 10mo 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.
- deleted 10mo ago[deleted]
- altruios 10mo agoWhat tools does the LLM have access to that would reveal sub-token characters to it? This assumes the colorblind person both believes it is true that they are colorblind, in a world where that can be verified, and possesses tools to overcome these limitations. You have to be much more clever to 'see' an atom before the invention of a microscope, if the tool doesn't exist: most of the time you are SOL.