David Rose Abducing That ChatGPT Learnt The Language System By Experiencing Instances Of Its Features

David Rose wrote to Sysfling on 24 Mar 2023 at 23:16:

My own contributions have been merely observations, using the tools of systemic functional semiotic text analysis.

I observe that the texts produced by the machine instantiate semiotic systems. To be able to do this, we are told the machine reads 1000s of texts, i.e. other instances of these systems. It is reasonable to abduce that the machine has learnt these systems by experiencing multiple instances of their features (not just the fields it gleans from Wikipedia), given our language based theory of learning.

The people programming the machine, with ‘reasoners’ as Mick puts it, have no more conscious knowledge of these systems and the processes of realisation and re-instantiation, than the machine does.

The machine itself tells us that its understanding of its “self” is ‘based purely on symbols and algorithms’. This resonates with your insistence that all it is doing ‘is producing nonrandom sequences of characters’. My analogy of a closed book was intended to evoke the contrast between the material recording of characters and the semiotic reading of those characters as instantiating expression systems, that realise content systems, that realise register and genre systems. …

My point is that all the semiotic systems instantiated in the texts it produces are ‘not learned in any direct way’. Neither the machine nor the “tech gurus” that program it can explain this to our satisfaction. The publications that you cite are undoubtedly illuminating, but our contribution can only be based on text analysis, which I submit will produce very different (possibly complementary) explanations.

Blogger Comments:

[1] To be clear, this is not a reasonable abduction, because it is nowhere near the "best available" conclusion to infer.

Abductive reasoning (also called abduction, abductive inference, or retroduction) is a form of logical inference that seeks the simplest and most likely conclusion from a set of observations. It was formulated and advanced by American philosopher Charles Sanders Peirce beginning in the last third of the 19th century. Abductive reasoning, unlike deductive reasoning, yields a plausible conclusion but does not definitively verify it. Abductive conclusions do not eliminate uncertainty or doubt, which is expressed in retreat terms such as "best available" or "most likely".

ChatGPT uses the lexical collocation frequencies in its database. While it is true that these frequencies instantiate the probabilities in the language systems of the people who wrote the texts, there is no evidence to support the claim that ChatGPT is using systems of features in producing its own texts. It just uses lexical collocation frequencies.

"Our language-based theory of learning" does not apply here, because the learning and "experiencing" of ChatGPT are material processes, not the mental processes of a language learner.

[2] To be clear, the argument here is that, since neither humans nor ChatGPT have conscious knowledge of the language system, both must use that system to produce texts. Clearly, a lack of awareness of X does not logically entail the presence of X.

[3] To be clear, here Rose is referring to Martin's self-contradictory misunderstanding of stratification, wherein functional varieties of language are modelled as context, instead of language, despite being instantiated as language (text). In SFL Theory, registers are context-specific varieties of language, viewed from the system pole of the cline of instantiation. Martin's genre, on the other hand, is scattered across SFL's architecture of language. As text type, genre is register viewed from the instance pole of the cline of instantiation, as purpose, genre is rhetorical mode (narrative etc.), and its structures are of the semantic stratum, though not organised according to metafunction.

[4] To be clear, a contribution that is only based on text analysis is a very limited contribution indeed. It is an understanding of SFL theory that has the potential of providing valuable insights into the issues raised by the coherence of texts produced by ChatGPT.