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

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

In recent discussions, large language models (LLMs) have often been humorously criticized for seemingly failing simple tasks—like counting the number of times a specific letter appears in a word, for example, trying to determine how many “R”s are in “Strawberry.” While these missteps might appear comical, they reveal underlying aspects of how LLMs process and interpret language.

How Do LLMs Process Text?

At their core, LLMs operate by transforming textual input into a series of small segments known as “tokens.” These tokens—often subword units—are then converted into numerical representations called “vectors.” These vectors travel through the model’s layers, enabling it to generate responses, predictions, or classifications.

Why Can’t LLMs Count Letters?

Unlike humans, who interpret text at a character level, LLMs are not explicitly trained to recognize individual letters or count their occurrences. Their internal representations are designed to capture contextual and semantic information rather than precise character-by-character details. Consequently, the vector representations omit specific letter counts, which explains why the models might overlook how many “R”s are in “Strawberry” or similar words.

Visualizing the Concept

For a more detailed understanding, check out the accompanying diagram here: https://www.monarchwadia.com/pages/WhyLlmsCantCountLetters.html. Please note that images cannot be posted directly within this platform, but the resource offers valuable insight into the inner workings of LLMs related to this topic.

Final Thoughts

While LLMs are incredibly powerful for understanding and generating language, their architecture is fundamentally different from the way humans process text. Tasks that require precise, character-level counting are often outside their designed capabilities, leading to fascinating—and sometimes amusing—errors along the way.