Why powerful generative AI models are bad at simple math like counting

AI’s Unexpected Stumbling Block: Large Language Models (LLMs) like ChatGPT and Claude, despite their advanced capabilities, struggle with simple tasks such as counting letters in words, revealing fundamental limitations in their processing methods.

The Irony of AI Capabilities: While concerns about AI replacing human jobs are widespread, these sophisticated systems falter at basic tasks that humans find trivial.

• LLMs fail to accurately count the number of “r”s in “strawberry,” “m”s in “mammal,” or “p”s in “hippopotamus.”
• This limitation highlights the difference between AI’s pattern recognition abilities and human-like reasoning.

Understanding LLM Architecture: The root of this counting problem lies in how LLMs process and understand language.

• LLMs are built on transformer architectures that use tokenization to convert text into numerical representations.
• Words are broken down into tokens, which may not correspond to individual letters, making letter counting challenging.
• For example, “hippopotamus” might be tokenized as “hip,” “pop,” “o,” and “tamus,” losing the connection to individual letters.

The Tokenization Conundrum: This process of breaking words into tokens is both a strength and a weakness for LLMs.

• Tokenization allows LLMs to predict and generate coherent text based on patterns.
• However, it hinders their ability to perform tasks that require understanding at the individual letter level.
• Current transformer architectures are not computationally feasible for processing individual letters without tokenization.

LLM’s Text Generation Process: The way LLMs generate responses contributes to their inability to count letters accurately.

• LLMs predict the next word based on previous input and output tokens.
• This method excels in generating contextually aware text but falls short in simple arithmetic tasks like counting letters.
• When asked to count letters, LLMs attempt to predict an answer based on the structure of the input sentence rather than performing the actual counting task.

A Workaround for Letter Counting: Despite their limitations, LLMs can be guided to perform these tasks indirectly.

• LLMs excel at understanding structured text, particularly computer code.
• Asking an LLM to use a programming language (like Python) to count letters typically yields correct results.
• This approach can be extended to other tasks requiring logical reasoning or arithmetic computation.

Implications for AI Integration: Understanding these limitations is crucial for responsible AI usage and setting realistic expectations.

• The letter counting experiment exposes LLMs as pattern-matching predictive algorithms rather than true “intelligence” capable of human-like reasoning.
• Recognizing these constraints is essential as AI becomes increasingly integrated into daily life and various industries.

Looking Ahead: AI Development and Limitations: The struggle with simple tasks like letter counting serves as a reminder of the current state of AI technology.

• While LLMs continue to impress with their language generation and task completion abilities, they still lack fundamental reasoning capabilities that humans possess.
• This limitation underscores the importance of ongoing research and development in AI to bridge the gap between pattern recognition and true understanding.
• As AI technology advances, it’s crucial to maintain a balanced perspective on its capabilities and limitations, ensuring its responsible and effective integration into various applications.