Original tweet by @emollick: https://twitter.com/emollick/status/1669939043243622402
Tweet text: One reason AI is hard to “get” is that LLMs are bad at tasks you would expect an AI to be good at (citations, facts, quotes, manipulating and counting words or letters) but surprisingly good at things you expect it to be bad at (generating creative ideas, writing with “empathy”).
it can be okay at general summary of stuff but bad when going into detailed facts. the bing search thing on chatgpt is really good tough.
?? Literally the entire purpose of the transformer architecture is to manipulate text, how is it bad at that? Am I misunderstanding this? Summarization, thematic transformation, language translation etc are all things AI is fantastic at…
The problem is that they “see” the text at the token level instead of the level of characters. That’s why they are bad at reversing strings or counting characters, for example. They perceive tokens as the atomic units of text instead of characters. For example, see how this comment gets tokenized:
With the token IDs shown:
The current ChatGPTs got pretty good at these tasks but they are still hard for them.
Here is an example of a (admittedly more complicated) character-level task failing:
Source: https://www.reddit.com/r/ChatGPT/comments/11z9tuk/chatgpt_vs_reversed_text/ (It’s from the devil’s website, so don’t open it)
Related tweet by @karpathy:
https://twitter.com/karpathy/status/1657949234535211009
Text reversing example from a tweet by @npew:
EDIT: sorry for the infodump, I just find these topics fascinating.
