Understanding Why LLMs Can’t Count the R’s in “Strawberry” (Variation 126)

Understanding Why Large Language Models Struggle to Count Letters in Words

In recent discussions, many have noticed that Large Language Models (LLMs) often falter when asked simple questions like determining how many times a particular letter appears in a word—for example, counting the number of R’s in “Strawberry.” This phenomenon raises an important question: why do these advanced models stumble on such seemingly straightforward tasks?

At the core, LLMs process input text by segmenting it into smaller units called “tokens.” These tokens might be words, subwords, or individual characters, depending on the model’s design. Once tokenized, the model transforms these units into high-dimensional numerical representations known as “vectors.” These vectors serve as the foundational input that guides the model’s understanding and response generation.

However, a critical limitation arises here. Unlike humans, who can directly count and remember specific letters within a word, LLMs are not inherently designed for precise character-level memorization. Their vector representations encode contextual and semantic information rather than detailed character counts. As a result, when asked to identify the number of R’s in “Strawberry,” the model doesn’t explicitly track each occurrence of a particular letter. Instead, it relies on its probabilistic understanding of language, which may not be precise enough to perform such detailed counts.

This nuance explains why LLMs often “miss the mark” on tasks requiring exact letter counting. Their architecture and training focus on predicting the next word or understanding context, not on meticulous character enumeration.

For a more in-depth explanation and a helpful visual illustration, visit this resource: https://www.monarchwadia.com/pages/WhyLlmsCantCountLetters.html.

Understanding these foundational limitations helps set realistic expectations for what LLMs can and cannot do—or where they might need additional mechanisms or training to handle specific tasks effectively.