Why AI can't spell 'strawberry'
How many times does the letter "r" appear in the word "strawberry"? According to formidable AI products like GPT-4o and Claude, the answer is twice.
How many times does the letter “r” appear in the word “strawberry”? According to formidable AI products like GPT-4o and Claude, the answer is twice.
Large language models (LLMs) can write essays and solve equations in seconds. They can synthesize terabytes of data faster than humans can open up a book. Yet, these seemingly omniscient AIs sometimes fail so spectacularly that the mishap turns into a viral meme, and we all rejoice in relief that maybe there’s still time before we must bow down to our new AI overlords.
The failure of large language models to understand the concepts of letters and syllables is indicative of a larger truth that we often forget: These things don’t have brains. They do not think like we do. They are not human, nor even particularly humanlike.
Most LLMs are built on transformers, a kind of deep learning architecture. Transformer models break text into tokens, which can be full words, syllables, or letters, depending on the model.
“LLMs are based on this transformer architecture, which notably is not actually reading text. What happens when you input a prompt is that it’s translated into an encoding,” Matthew Guzdial, an AI researcher and assistant professor at the University of Alberta, told TechCrunch. “When it sees the word ‘the,’ it has this one encoding of what ‘the’ means, but it does not know about ‘T,’ ‘H,’ ‘E.’”
This is because the transformers are not able to take in or output actual text efficiently. Instead, the text is converted into numerical representations of itself, which is then contextualized to help the AI come up with a logical response. In other words, the AI might know that the tokens “straw” and “berry” make up “strawberry,” but it may not understand that “strawberry” is composed of the letters “s,” “t,” “r,” “a,” “w,” “b,” “e,” “r,” “r,” and “y,” in that specific order. Thus, it cannot tell you how many letters — let alone how many “r”s — appear in the word “strawberry.”
This isn’t an easy issue to fix, since it’s embedded into the very architecture that makes these LLMs work.
TechCrunch’s Kyle Wiggers dug into this problem last month and spoke to Sheridan Feucht, a PhD student at Northeastern University studying LLM interpretability.
“It’s kind of hard to get around the question of what exactly a ‘word’ should be for a language model, and even if we got human experts to agree on a perfect token vocabulary, models would probably still find it useful to ‘chunk’ things even further,” Feucht told TechCrunch. “My guess would be that there’s no such thing as a perfect tokenizer due to this kind of fuzziness.”
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I tested that and it's super funny lol
Felt like I broke the AI
I blame @bradleyarrow.
That could be a new southpark ep.
yea I knew he was up to something