Comment by ricardobeat
2 hours ago
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.
2 hours ago
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.
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.
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.
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.
we already fixed it with reasoning
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.