Understanding Why Large Language Models Cannot Count the Letter ‘R’ in “Strawberry” (Variation 155)

Understanding Why Large Language Models Struggle to Count Letters: The Case of “Strawberry”

In the world of artificial intelligence, large language models (LLMs) like GPT often face criticism for seemingly simple tasks—such as counting the number of times a specific letter appears in a word. A common example is the question: “How many R’s are in ‘Strawberry’?”

So, why do these models stumble on such straightforward queries?

The core reason lies in how LLMs process text. When an input string is fed into a language model, it is first converted into smaller units called “tokens.” These tokens can be words, parts of words, or even individual characters, depending on the tokenization method used. However, most large models primarily operate on token sequences that are converted into numerical representations known as “vectors.” This process involves transforming each token into a high-dimensional array of numbers that encode contextual information.

Crucially, LLMs are not explicitly trained to count individual characters within words. Their focus is on understanding and predicting sequences of tokens at a semantic and syntactic level rather than performing explicit character-by-character counting. As a result, the model’s internal representations—these vectors—do not maintain a precise, character-level memory of the original text. Instead, they capture statistical patterns, contextual associations, and semantic relationships, which are fundamentally different from exact letter counts.

In essence, this explains why LLMs often fail at tasks like counting R’s in “Strawberry”—they are designed for language understanding and generation, not detailed letter tallies.

For a more visual explanation, check out the detailed diagram available here: https://www.monarchwadia.com/pages/WhyLlmsCantCountLetters.html. (Please note, image sharing may be restricted on certain platforms.)

Understanding these limitations highlights the importance of context and the scope of what large language models are built to do. While they excel at many language tasks, precise character-level operations remain outside their primary capabilities.