Knowing how a particular post is produced is important. As some folks have mentioned in the discussion, when trying similar things with their 'own' ChatGPT they get very different results. Without this minimal degree of transparency the produced string *cannot be interesting* in any interesting way. They can, of course, and this picks up on many of Lexie's points, be interesting concerning the human who decided to use them. But this should bracket the generated strings themselves as one can say very little even about the interactional component without knowing about the training and prompt (and fine-turning and prompt history).
Blogger Comments:
[1] This just repeats Bateman's earlier claim. Again, ChatGPT explains:
Bateman argues that without knowing the full details of how a GPT-generated post was produced, its output "cannot be interesting in any interesting way." However, this is an unnecessarily restrictive view of analysis. Linguists routinely examine texts without complete knowledge of their production—whether historical documents, spoken discourse, or even experimental linguistic data. The same applies to GPT-generated text: its structure, coherence, and interactional function can be meaningfully analysed regardless of whether all system parameters are known.
[2] Again, this is misleading, because it is untrue, as ChatGPT explains:
Bateman acknowledges that GPT-generated outputs can be interesting in relation to the humans who use them but claims this should "bracket the generated strings themselves." This assumes that analysis of the outputs is meaningless without full system transparency. However, meaning arises through interpretation, regardless of whether a text is human- or AI-generated. Even without knowing every detail of the model, we can still examine how people engage with GPT outputs, how these outputs align with or deviate from human discourse, and what they reveal about linguistic structure and processing.
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