Understanding Why Large Language Models Struggle to Count Letters in Words
In recent discussions, a common point of curiosity—often accompanied by humorous mockery—revolves around why large language models (LLMs) sometimes fail to perform simple counting tasks, such as determining the number of ‘R’s in the word “Strawberry.” So, what underlies this limitation?
At their core, LLMs process text by first dividing input into manageable segments known as “tokens.” These tokens are then transformed into numerical representations called “vectors,” which serve as the foundation for the model’s subsequent processing layers. Importantly, this approach emphasizes understanding language at a broader, semantic level rather than at the granular, character-by-character scale.
Since LLMs are not specifically trained to recognize or count individual letters within words, their vector representations do not preserve precise information about each character. This means that the models lack a reliable internal record of individual letter counts—such as how many times ‘R’ appears—leading to errors in tasks that require exact letter counting.
For a more detailed explanation, including visual diagrams, please visit this informative resource: https://www.monarchwadia.com/pages/WhyLlmsCantCountLetters.html. (Please note that, due to platform restrictions, images from this page cannot be directly embedded here.)
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