Understanding Why Large Language Models Fail to Count the R’s in “Strawberry”

Understanding Why Large Language Models Struggle to Count Letters in Words

In recent discussions, it’s become a common joke that Large Language Models (LLMs) like GPT can’t accurately count the number of certain letters in a word—take, for example, the number of “R’s” in “Strawberry.” But what’s the underlying reason for these seemingly simple errors?

At their core, LLMs process text by breaking it down into smaller units called tokens. These tokens often represent chunks of words, entire words, or subword segments, rather than individual characters. Once tokenized, these units are transformed into sophisticated numerical representations known as vectors. These vectors are then used as the input for subsequent layers of the model to generate responses or perform tasks.

One key factor is that LLMs are primarily trained to understand and generate coherent language patterns, not to perform precise letter-level counting. Since their internal representations focus on patterns at the token or word level rather than explicit character-by-character tracking, they lack a direct, explicit memory of individual letter counts. As a result, when asked to count specific letters within words, the models often produce incorrect answers because they do not inherently “know” the number of R’s in “Strawberry.”

This limitation explains why simple tasks like counting characters can be surprisingly challenging for models designed for language understanding rather than precise counting. For a more visual explanation of this concept, check out this detailed diagram here. (Please note that this site contains helpful illustrations but does not permit posting images directly.)

In essence, the reasons behind these quirks highlight the fundamental differences between human language comprehension and the way Large Language Models process text. While LLMs excel at understanding context and generating human-like responses, they are not specialized for precise, character-level calculations.