Note that when there are places in the generated strings that require knowledge, things start going wrong. And the very real danger is that this is not at all obvious unless one has the requisite knowledge in the background.I tried to show this by providing some more detail about how the language models actually work; the generated response that Chris posted showed that it or he hadn't actually much of an idea of what was being talked about. And this will be the same for *any area addressed*. To the extent that the text appears to be making interesting points, these will be where a generous interpretation of what appears to be being said aligns with one's own views or brings to mind interesting other lines of thought.
Blogger Comments:
[1] Here Bateman makes a sweeping overgeneralisation, as ChatGPT makes clear:
Bateman claims that "when there are places in the generated strings that require knowledge, things start going wrong" and that this applies to "any area addressed." This is demonstrably false. LLMs often produce accurate and well-formed responses in knowledge-heavy areas, depending on the training data and the specificity of the query. While errors do occur, a blanket claim that all knowledge-dependent content from ChatGPT fails is simply untrue.
[2] Here Bateman discredits ChatGPT texts without providing supporting argument. ChatGPT:
Bateman suggests that your post demonstrated a lack of understanding of "what was being talked about" without actually engaging with the specifics. This is a classic rhetorical evasion—asserting failure without substantiating it. If there were actual errors in the generated response, why not point them out directly rather than making vague accusations?
[3] Here Bateman is redefining "meaning" so narrowly that only his own interpretations count. ChatGPT explains:
Bateman suggests that LLM-generated text only seems meaningful because readers project meaning onto it. He argues that people find such text "interesting" when they interpret it generously—either because it happens to align with their own views or sparks new ideas. This downplays the fact that all interpretation involves some degree of reader engagement. The real question is whether the text provides enough structure to guide interpretation in a way that resembles meaningful human communication.
By claiming that LLM outputs only "appear" meaningful when readers are being overly generous, he's setting up a definition of meaning that excludes any interpretation that doesn't align with his own criteria. This lets him dismiss any insights drawn from LLM-generated text as mere projection rather than genuine meaning-making.
