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

Understanding Why Large Language Models Struggle with Letter Counting: The Case of “Strawberry”

In recent discussions, many have pointed out that Large Language Models (LLMs) often stumble when asked simple questions like identifying how many times the letter “R” appears in the word “Strawberry.” This has led to curiosity and some skepticism about their capabilities. So, what exactly causes this limitation?

At their core, LLMs process text by first dividing written content into smaller, manageable units called “tokens.” These tokens are then transformed into numerical representations known as “vectors.” These vectors serve as the foundational inputs that drive the model’s understanding and response generation.

However, because LLMs are not explicitly trained to recognize or count individual characters, their internal representations do not preserve exact, character-level information. Instead, they focus on understanding contextual patterns within language, which means that fine-grained details—such as the number of specific letters in a word—are not reliably retained. Consequently, when asked to count the R’s in “Strawberry,” the model’s architecture doesn’t support precise character tracking, leading to errors in such tasks.

For a more detailed visualization of this process, visit this informative diagram: https://www.monarchwadia.com/pages/WhyLlmsCantCountLetters.html. (Please note, image sharing isn’t permitted in all forums, but the diagram provides clear insights into this topic.)

Understanding this limitation helps us appreciate the design and training scope of LLMs—it’s not their purpose to perform exact character counts but to grasp and generate language based on contextual patterns. Recognizing these nuances allows for better expectations and more targeted development in the field of AI language modeling.