Large language models like ChatGPT excel in generating language but struggle with straightforward tasks, such as counting letters, highlighting their limitations in human-like reasoning.
Although powerful, LLMs' inability to count specific letters in words like 'strawberry' underscores that they do not 'think' or process information as humans do.
The architecture of LLMs relies on tokenization, converting text into numerical representations to analyze patterns, which might explain their faults in counting tasks.
These systems, based on transformer design, don't memorize words but rather engage in statistical pattern recognition, leading to their failures in simple counting tasks.
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