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Comment by ricardobeat | Hacker News Reader

Calling it a 'failure mode' implies it could be fixed. This is an inherent flaw in how LLMs work and will never go away until some new kind of architecture that can actually "read text" comes along.

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It seems that it can be fixed by simply doing away with Byte Pair Encoding tokenization.

Byte Latent Transformer - https://arxiv.org/abs/2412.09871

1.1% vs 99.9% on a vanilla vs byte latent transformer on a CUTE Spelling benchmark. Char and Word manipulation benchmarks also saw huge gains.

One "fix" is for the caller to correctly classify those fundamentally impossible tasks and pass them to a subprocess.

Some future "AI" could be a billion benchmark-hacks and a way to tell which one is needed.

I mean, for what it's worth, when I need to multiply two numbers, I mentally call the "multiply numbers" algorithm stored in my head, then sit down and work through the process on paper.

I've got no problem with an AI doing something similar

Seems fairly trivially fixable to me, e.g. by allowing the LLM to call a tool to spell out a word.

... assuming you build the tool and then think that it's worth polluting context with making that tool available, and then that the LLM decides to actually use the tool. Tool parameter space and tool selection still remains a complicated topic.

Yes, they can already do this by writing code, and you can train them to know how/when to do this. Fundamentally though, it’s still a “what color is the air” type of question after tokenization.

They're not fundamentally unsolvable - even bigger networks with even more training can simply be trained to give the correct answers to all of these questions.

If you want it to read letters, all you have to do is make your tokens be letters. That's easier than normal tokenization.

Which is a new architecture.

It's not "some new kind of architecture comes along". It's been done before many many times and it's a very basic change, turning off a specific optimization. And it's a perfectly good style of LLM so "inherent flaw" isn't true either.

And yet nobody is doing that… big-token conspiracy

"nobody is disabling this optimization" shouldn't be surprising. It's a matter of knowing what letters are in words versus a 4x token efficiency boost. Nobody cares enough about spelling.

If it was top priority, every company that can't find a post training fix would go disable half their tokenizer code and it would be solved in the next model.

we already fixed it with reasoning

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