alt Hacker News> due to fundamental limitations
People keep throwing this phrase around in relation to LLMs, when not a single “fundamental limitation” has been rigorously demonstrated to exist, and many tasks that were claimed to be impossible for LLMs two years ago supposedly due to “fundamental limitations” (e.g. character counting or phonetics) are non-issues for them today even without tools.
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>People keep throwing this phrase around in relation to LLMs, when not a single “fundamental limitation” has been rigorously demonstrated to exist
Some limitations are not rigorously demonstrated to be fundamental, but continuously present from the first early LLMs yes. Shouldn't the burden of proof be on those who say it can be done?
And some limitations are fundamental, and have been rigorously demonstrated, e.g.:
Character counting remains a huge issue without tools.
Are you using only frontier models that are gated behind openai/anthropic/google APIs? Those use tools to help them out behind the scenes. It remains no less impressive, but I think we should be clear.
The literal best public models still fail to count characters consistently in practice so I’m not sure what you mean. It’s literally a problem we’re still trying to solve at work
Is character counting actually not an issue anymore? Do you know somewhere where I can read more about this?
Character counting errors are a side effect of tokenization, which is a performance optimization. If we scaled the hardware big enough we could train on raw bytes and avoid it.
Your comment, after removing the particulars, has a shape of:
People have an <opinion> which hasn't been rigorously proven, while <not rigorously proven counteropinion>.
As such, I am not sure what you're trying to achieve here.
Drawing five fingered humans was a fundamental limitation... until it's not.
This is kind of my point, we need to get better at describing the limitations and study them. It seems extremely clear that there are limitations, and not just temporary ones, but structural limitations that existed at the beginning and continue to persist.
If you remove the auxiliary tools and just leave the core LLM then strawberry still has an undefined number of `r`s in it.
of course, if you choose to ignore all the limitations they indeed have no limitations.
> character counting
The models now whaste a vast amount of useless neurons memorising the character count the entire English language so that people can ask how many r's are in strawberry and check a tickbox in a benchmark.
The architecture cannot efficiently or consistently represent counting letters in words. We should never have forced trained them to do it.
This goes for other more important "skills" that are unsuited to tranformer models.
Most models can now do decent arithmetics. But if you knew how it has encoded that ability in its neurons then you would never ever ever ever trust any arithmetic it ever outputs, even in seems to "know" it (unless it called a calculator MCP to achieve it).
There are fundamental limitations, but we're currently brute forcing ourselves through problems we could trivially solve with a different tool.