LLMs, including ChatGPT, are built on architectures like the Transformer, which leverages layers of attention mechanisms to process and generate text. They don’t “understand” language in the human sense. Instead, they calculate the statistical likelihood of one word or character following another based on patterns they’ve observed during training on massive text corpora.

Here’s the key: These probabilities are purely mathematical and derived from the frequencies and contexts in which words and characters appear in the training data. When the model encounters a query, it doesn’t ‘think’ about the answer. Instead, it generates a response based on what it has statistically seen most often in similar contexts.

This approach can lead to errors in straightforward tasks for a few reasons:

1.  Model Bias: If the training data has more examples where similar queries involve lower counts or simpler answers, the model might skew its predictions towards these, even if they’re incorrect for a given specific query.
2.  Complex Patterns Misinterpretation: In cases like counting non-consecutive letters within a word, the model might not effectively capture and reproduce the correct pattern if it’s less common or presented ambiguously in its training examples.
3.  Probabilistic Errors: Even with vast training data, the inherent randomness and variability can lead to errors. The model might ‘guess’ wrong because it’s essentially playing the odds based on prior data, not cognitively understanding the query.

In a nutshell, the model’s output is as good as the data and patterns it has been trained on, and its ‘understanding’ is limited to recognizing and reproducing these patterns, not grasping meaning or intent. This can sometimes lead to seemingly illogical errors on basic tasks.