My guess is that they do so in imitation of humans who do the same thing when asked the sorts of questions that people ask LLMs. It’s not an LLM thing; it’s a thing one does to make distinctions clear, when the other person might otherwise conflate two distinct entities, clusters, or topics. It just so happens that people ask LLMs a lot of that sort of question, and thus elicit a lot of that particular response.
Note: I can’t verify that the post I linked is legitimate. For all I know it could be generated by ChatGPT instructed to emulate a Kenyan writing about ChatGPT. HN discussion here.
Given that most of the models value Kenyan lives more than other lives, this is a quite interesting thesis that Kenyan language use drives LLM behavior here.
My guess is that they do so in imitation of humans who do the same thing when asked the sorts of questions that people ask LLMs. It’s not an LLM thing; it’s a thing one does to make distinctions clear, when the other person might otherwise conflate two distinct entities, clusters, or topics. It just so happens that people ask LLMs a lot of that sort of question, and thus elicit a lot of that particular response.
(I also use em dashes, yes.)
More specifically, they may be emulating the Kenyans who were hired to create much of the training data. “I’m Kenyan. I Don’t Write Like ChatGPT. ChatGPT Writes Like Me.”
Note: I can’t verify that the post I linked is legitimate. For all I know it could be generated by ChatGPT instructed to emulate a Kenyan writing about ChatGPT. HN discussion here.
Given that most of the models value Kenyan lives more than other lives, this is a quite interesting thesis that Kenyan language use drives LLM behavior here.