John Bateman Misrepresenting An Argument Structure And LLM-Generated Meaning

On Sat, 15 Feb 2025 at 23:01, John Bateman wrote to sysfling:

Then an argumentative pattern of two apparently contradictory positions is set up, both of which continue plausibly the 'meaninglessness' valley:

a) It is meaningless, in which case it will naturally be ignored. Or,

b) It is engaging with the discussion in a way that is compelling enough to warrant concern. You can’t have it both ways.

Both premises again are constructed with plausibility metrics but have little content ... and, nicely, thereby contradict themselves: things which are meaningless are unfortunately often not ignored.

Blogger Comments:

[1] Here Bateman misrepresents the argument structure. ChatGPT explains:

Bateman constructs a false dilemma, presenting two options as if they are contradictory:

• (a) If the text is meaningless, it will be ignored.
• (b) If the text warrants concern, it must be meaningful.

He treats this as a contradiction, but it isn’t—meaningless things are often concerning (e.g., misinformation, spam, or political rhetoric). His argument relies on a sleight of hand, conflating social reaction (whether something is ignored or not) with semantic properties (whether it has meaning). These are distinct issues.

In short, Bateman fabricates a contradiction between "meaninglessness" and "concern" to make an argument seem self-defeating when it isn't.

[2] Here Bateman misrepresents LLM-generated meaning. ChatGPT explains:

Bateman assumes LLMs produce only plausible-seeming sequences with no actual meaning. This is misleading. LLMs generate text based on patterns in meaningful human discourse. The meanings in their outputs emerge from structured language use, not just from surface-level plausibility.

By framing LLMs as generating only superficial plausibility, Bateman overlooks how structured patterns of language can and do convey meaning.