Let's build the GPT Tokenizer

ChatGPT is specifically bad at these kinds of tasks because of tokenization. If you plug your query into [link] , you can see that"egregious" is a single token, so the LLM doesn't actually see any "e" characters -- to answer your question it would have had to learn a fact about how the word was spelled from it's training data, and I imagine texts explicitly talking about how words are spelled are not very common. Good explanation here if this still doesn't make sense: [link] , or check out Andrej Karpathy's latest video if you have 2 hours for a deep dive: [this video] IMO questions about spelling or number sense are pretty tired as gotchas, because they are all basically just artifacts of this implementation detail. There are other language models available that don't have this issue. BTW this is also the reason DALL-E etc suck at generating text in images.

Andrej Karpathy goes into quite some depth about how tokenization works here: [this video] tl;dr many of the LLMs use byte-pair encoding to create tokens. You take a set of documents, and then form tokens by repeatedly merging the most common pair of tokens. The initial set of tokens is 256 raw bytes. And the text is typically represented in utf-8. I expect that although the LLMs can understand arbitrarily but cleanly offset unicode code points by (eventually) noticing the final byte of each sequence, they would do markedly worse on actually processing and completing on them, because they will not have been reduced to the normal set of tokens. However, if the text is actually output cleanly converted, either in internal thinking tokens or in the beginning of the response, they should do fine. Understanding tokenization is surprisingly useful, even if that video seems awfully long to devote to such a tedious subject. Even Karpathy doesn't like it!