Why LLM’s can’t count the R’s in the word “Strawberry”

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

In recent discussions, many have pointed out that large language models (LLMs) often falter when asked simple tasks like counting the number of times a specific letter appears in a word—consider the classic example: “How many R’s are in ‘Strawberry’?”

This common curiosity highlights a fundamental aspect of how LLMs process language. Unlike human readers, who can easily scan a word and count individual letters, LLMs do not inherently “know” the structure of the text they analyze at the character level. Instead, these models operate on a sophisticated process involving tokenization and vector representations.

The Inner Workings of LLMs

When an LLM receives input text, it first decomposes the text into smaller components called tokens. These tokens might be words, subwords, or characters, depending on the tokenization scheme. Subsequently, each token is transformed into a numerical vector—a set of numbers that encodes its meaning and context—through a process called embedding.

These vectors are then processed through multiple layers of the model to generate predictions or responses. It’s important to note that this entire process focuses on capturing semantic and syntactic relationships at a broader level, rather than maintaining a detailed, character-by-character memory.

Why Counting Letters Isn’t Within the Model’s Capabilities

Because LLMs lack an explicit mechanism to track individual characters within words, they don’t “remember” the precise number of specific letters such as R’s in “Strawberry.” Their training is oriented towards understanding language patterns, contexts, and meanings—rather than performing exact letter counts. As a result, tasks that require detailed, low-level textual analysis often fall outside their effective scope.

Visualizing the Concept

For a clearer understanding, a helpful diagram illustrates the tokenization and vectorization process, shedding light on why LLMs excel at language understanding but stumble with simple character counts. (You can find this diagram here. Please note that images cannot be embedded directly in this post.)

In Summary

While large language models are incredibly powerful in understanding and generating human-like text, their structure and training make them ill-suited for tasks that demand precise, character-level counting. Recognizing these limitations helps set realistic expectations and guides us toward designing specialized tools for low-level textual