Friday, 12 May 2023

David Kellogg On Mathematical Equations [1]

David Kellogg wrote to sys-func on 24 April 2023 at 12:00:

I remember being vaguely dissatisfied with a presentation that Yaegon gave on this topic at University of Sydney, simply because he over-stressed the difference between mathematics and spoken language and at one or two points even argued for distinct origins in different kinds of logic. But now I see his point.

The terms "coding" and "decoding" imply asymmetry, and in fact asymmetry is the sine qua non of Halliday's understanding of relational processes. Understanding the grammar of a statement like "Tom is the tall one" (value-to-token, or encoding) or "Tom is the treasurer" (decoding) is really understanding that the sentence is NOT redundant — that the expressions are quite different when you reverse them. Encoding and decoding sentences are no more reversible than part-whole relationships, e.g. "All donkeys are animals" and "all animals are donkeys".  

But mathematical equations do not in themselves imply this asymmetry. For example, your equation could be reversed with very little or no difference in meaning,  "All donkeys are animals" is true and "all animals are donkeys" is false. But "c squared equals a squared plus b squared" is just as true and just as false as "a squared plus b squared equals c squared".

I was just discussing this with my father, who has been working on some data from the Parker Solar Probe, now entering the sun's corona. He wants to demonstrate that the heating of the solar wind  is caused (or at least could be caused) by the damping of ion acoustic waves in the solar wind. He pointed out to me that his papers are usually written in a mixture of mathematics and English. Here, for example, he is discussing how to determine the motion of a particle in an electric field. He says::

"dv/dt  = eE/m   

"So that

"v= eE/m "

Because the equations are embedded in language, they are not reversible. But the non-reversibility (that is, the fact that they are decoding relationships, with the token on the left and the value on the right) is an artefact of the language and not a logical consequence of the mathematics, just as the order of "a squared plus b squared equals c squared" is an artefact of our alphabet. 

 

Blogger Comments:

[1] This is misleading, because Halliday (1985, 1994) does not discuss relational processes in terms of asymmetry. What Kellogg refers to as 'asymmetry' is the functional difference between the two participants of an identifying process: Token vs Value and Identified vs Identifier. In a decoding clause, the asymmetry is Token/Identified vs Value/Identifier, whereas in an encoding clause, the asymmetry is Token/Identifier vs Value/Identified.

[2] To be clear, understanding the grammar of identifying clauses is understanding the functions that each clause constituent realises: Token or Value, Identifier or Identified.

[3] To be clear, encoding and decoding are reversible, but changing the direction of coding changes the meaning that the identity construes: Value encoded by reference to Token vs Token decoded by reference to Value.

[4] To be clear, part-whole relationships are reversible, in terms of coding, when they are construed as identifying rather than attributive. For example, the sports centre comprises four buildings is decoding in contexts where four buildings identifies the sport centre, and encoding in contexts where the sport centre identifies four buildings.


[5] To be clear, attributive clauses are not reversible in terms of the direction of coding because coding is restricted to construals of identity, whereas attributive clauses construe class membership (Halliday & Matthiessen 1999: 145-6), not an identity. (Trivially, reversing the participants in all donkeys are animals yields animals are all donkeys.)

[6] This is misleading, because it is untrue. If the participants in the clause a squared plus b squared equals c squared are reversed, all that is preserved is the identity of a squared plus b squared and c squared. The reversal changes not only what serves as Token and Value, but what serves as Theme and Rheme and what serves as Subject and Complement. Moreover, it can also involve a reversal the direction of coding:







[7] To be clear, the equations are reversible, though the reversal changes the functional role served by each nominal group in the equation, as explained in [6].

[8] To be clear, what makes these equations decoding is the conflation of Value with Identifier, not the sequence of Token and Value. If the Token on the left is used to identify the Value on the right, these equations are encoding.

[9] To be clear, the ordering Token^Value is given by the clause being operative in voice. But this is insufficient in itself to make the clause decoding; see [8]

Importantly, the mathematical equation uses the operative identifying clause as its template, and solving an equation involves repeatedly elaborating the equation, while preserving the identity relation, until each unknown is construed as a Token that is decoded by a Value.

[10] To be clear, this is the Pythagorean equation. The letters and b stand for the two sides that meet at a right angle, and c stands for the hypotenuse.

Tuesday, 4 April 2023

David Rose On ChatGPT Recognising Canonical Structure

I’m just looking for the limits of GPT and whatever it is that ‘context’ is supposed be...
These are each instances of a pedagogic register type in which a learner solicits knowledge.
GPT recognises the canonical structure, even when it’s initiated with a statement...

 

 

speech

function

exch

role

register phase

interact

Mario

Your self is merely semiotic.

statement

K2

propose class

solicit knowledge

GPT

That is correct.

answer

K1

evaluate

approve

 

As an AI language model, my "self"

or programmed understanding of

my existence and capabilities is based

purely on symbols and algorithms. 

statement

K1

elaborate class

impart knowledge


Keep in mind that these are just labels for structures. We give them functional labels because that’s the relation we interpret between ranks and strata. But GPT doesn't have to interpret them as ‘functions’... just predict appropriate structures in response.

and later that day at 22:01:

But note that GPT is also negotiating affiliation with Mario... positioning itself outside of human communities... no doubt ‘trained’ by its developers.

 

Blogger Comments:

[1] To be clear, ChatGPT produces texts from the lexical collocation frequencies in a reservoir of instances, not from an individuated repertoire of systems that are realised as structures. On this basis, ChatGPT is not an individuated meaner.

[2] To be clear, by 'context' Rose means Martin's misunderstanding of register as a stratum of context. So here Rose is presenting a text (language) as an instance of a type of context, despite context being opposed to language in Martin's stratification model.

[3] To be clear, importantly, it is not ChatGPT that recognises 'the canonical structure' but Rose. See [1] above.

[4] To be clear, in SFL Theory, 'acknowledgement' is the expected response to a statement (Halliday & Matthiessen 2014: 137).

[5] To be clear, ChatGPT does not 'predict structures', because that is not how it operates; see [1] above.

[6] To be clear, ChatGPT is not 'negotiating affiliation', because ChatGPT is not an individuated meaner; see [1] above.

Monday, 3 April 2023

David Rose On What Makes Us Human And Fooled

David Rose wrote to Sysfling on 31 Mar 2023 at 9:23:
I was thinking of Shooshi’s what makes us human... dirty jokes ;-))
Re pickiness of humans... I have a sneaking suspicion GPT’s telling us something important about us, not just itself. How much are we fooled by our synoptic view of systems and texts? We can see emergent patterning when we stare at enough printed text, and then represent it as weighted options in systems. But how much do we actually know about how we process text above the lower ranks of expression? What actually is the relation between structural probabilities in text production and ‘meaning’.


Blogger Comments:

[1] To be clear, for Halliday, it is the stratified content plane of language that "makes us human". Halliday (2002 [1996]: 388):
The more complex type of semiotic system is that which evolves in the form of Edelman’s “higher order consciousness”. This higher order semiotic is what we call language. It has a grammar; and it appears to be unique to mature (i.e. post-infancy) human beings. In other words, it evolved as the “sapiens” in homo sapiens.
Halliday (2003 [1995]: 390, 430n):
In this paper I have tried to identify, and to illustrate, certain aspects of language which seem to me critical to a consideration of language and the human brain. In doing so I have assumed that language is what defines the brain of homo sapiens: what constitutes it as specifically human.
The emergence of grammar … is the critical factor in the development of higher-order consciousness; homo sapiens = homo grammaticus. See Halliday (1978a, 1979b); Painter (1984, 1989); Oldenburg (1986).
Halliday & Matthiessen (2014: 25):
This stratification of the content plane had immense significance in the evolution of the human species – it is not an exaggeration to say that it turned homo ... into homo sapiens (cf. Halliday, 1995b; Matthiessen, 2004a). It opened up the power of language and in so doing created the modern human brain. 

[2] To be clear, in the case of ChatGPT, "we" are not fooled by "our synoptic view of systems and texts", but by ascribing systems to an AI model of language that generates texts from (the lexical collocation probabilities of) instances, not systems.

[3] To be clear, this relation is given by the architecture of language proposed by SFL Theory: structures are specified systemically in the realisation statements attached to features whose probability of instantiation varies according to register. 'Meaning', in the narrower sense, is the stratum of semantics: its systems that are realised as structures, and instantiated as texts.

But importantly, ChatGPT does not use systems that specify structural probabilities to generate texts. Instead, it uses the lexical collocation probabilities garnered from a 'reservoir' of texts, each of which is the instance of the system of the meaner who produced it. (In lexicogrammar, collocation is the syntagmatic dimension of lexis, whereas structure is the syntagmatic dimension of grammar.)

Sunday, 2 April 2023

David Rose On Why Language Needs A Stratified Content Plane

The way GPT ‘reads’ and ‘writes’ text as probabilistic strings of tokens highlights the question of why language needs a stratified content plane. It sounds something like dynamic processes of DS, as foreshadowed by Firth’s notion of ‘mutual expectancy’. The multiple ‘levels’ of its design could enable it to simultaneously predict and review appropriate structures at several scales and various functions, e.g. figures and figure sequences, lexical strings, reference chains, method of development, evaluation prosodies, exchange roles... [just *predicting structures* not making meanings as persona semiotica].

But lang requires these various scales of DS processes to be (re)organised in local processes of LG. Why?


Blogger Comments:

[1] To be clear, it is not that language "needs" a stratified content plane, but that, on the SFL model, a stratified content plane is what distinguishes language from other socio-semiotic systems.

[2] To be clear, here Rose is merely promoting Martin's discourse semantics, which confuses Halliday's textual grammar (lexical cohesion, reference etc.), Fries' textual grammar (method of development), Halliday's ideational semantics (figures and sequences), inter alia.

More importantly, the dynamics of one stratum, discourse semantics, are irrelevant to the stratification of content, because stratification is the relation between levels of abstraction, and so what stratification affords is the decoupling of congruent relations between strata to open up the enormous semogenic potential of grammatical metaphor. Moreover, as Halliday & Matthiessen (1999: 237) point out:

If the congruent form had been the only form of construal, we would probably not have needed to think of semantics and grammar as two separate strata: they would be merely two facets of the content plane, interpreted on the one hand as function and on the other as form.

[3] This is misleading. Martin's discourse semantics is not organised into 'multiple levels' and 'several various scales'.

[4] On the one hand, this misunderstands the relation between strata, and on the other hand, it is very misleading. Semantics and lexicogrammar constitute different levels of symbolic abstraction, so the lower level (Token) is not a local reorganisation of the higher level (Value): the lower level (lexicogrammar) is a realisation of the higher level (semantics). 

In all of Martin's work, which Rose continually promotes, there is a failure to understand strata as different levels of symbolic abstraction. For example, Martin (1992) misunderstands strata as modules (of the same level of abstraction), and confuses stratification with semogenesis ('all strata make meaning').

The reason why this is misleading is that it presents Halliday's lexicogrammar as a "reorganisation" of Martin's discourse semantics, whereas, in terms of theorising, Martin's discourse semantics is a reorganisation of Halliday's lexicogrammar (cohesion) and semantics (speech function). 

Saturday, 1 April 2023

David Rose On The Reluctance To Divorce Language From Consciousness

A more general anxiety about relations between language and personhood exists in our own community. …

But SFL would not have progressed without separating out the systems from their uses and users.

One way our anxiety is expressed is a reluctance to divorce language from consciousness. Yet consciousness is a property of individual persons but language systems are a property of communities. They exist before, after and without the individual persons who use them.

By systems we mean both potential and actual – system and text. Instantiation is a relation between texts and systems, irrespective of persons.


Blogger Comments:

[1] To be clear, Halliday makes a useful distinction between 'person' as a social individual and 'meaner' (language user) as a socio-semiotic individual. Halliday & Matthiessen (1999: 610):
The human individual is at once a biological "individual", a social "individual" and a socio-semiotic "individual":
as a biological "individual", s/he is an organism, born into a biological population as a member of the human species.

as a social "individual", s/he is a person, bom into a social group as a member of society. "Person" is a complex construct; it can be characterised as a constellation of social roles or personae entering into social networks … .

as a socio-semiotic "individual", s/he is a meaner, born into a meaning group as a member of a speech community. "Meaner" is also a complex construct. 

[2] To be clear, this "progress" in SFL is Martin's confused model of individuation and affiliation (critiqued here). Martin et al (2013):


[3] To be clear, for neuroscientist Edelman (e.g 1992), it is language that distinguishes higher-order consciousness from the primary consciousness that humans share with many other species. For Halliday (Halliday & Matthiessen 1999), the theory of experience that has evolved in language equates the content plane of language with the content of consciousness. Ideationally, consciousness is the interior symbolic processing of sensing, and the exterior symbolic processing of saying, which create content through projection, and interpersonally, consciousness is the self enacted as meaner: as an interactant in exchanges.

[4] To be clear, on the SFL model, the collective nature of language entails that human consciousness is also collective. Halliday & Matthiessen (1999: 609):

Edelman's interpretation of higher-order consciousness referred to above suggests that this form of consciousness (unlike primary consciousness) is constituted in language. Language is a socio-semiotic system, so it follows that higher-order consciousness is constituted socio-semiotically; and since socio-semiotic systems are collective, it follows that higher-order consciousness must also be collective. Collective consciousness is an attribute of human social groups — the members of a given culture. But we need to distinguish between the consciousness of a social group and the consciousness of a species, whose collective construal of experience is codified in the structure of the brain. All human populations have the same brain, and to that extent all construe experience in the same way. But humans live in social groups, and their local environments vary one from the other; to that extent, different groups construe experience in different ways. The significance of this for us is that language is the resource for both: both what is common to the species as a whole, and what is specific to the given culture. In the way these two components are construed in the grammar, we cannot tell them apart. But it is the role of language in the construction of experience as meaning — as shared activity and collaboratively constructed resource — that gives substance to the concept of collective consciousness as an attribute of the human condition.

Moreover, here Rose even contradicts the models that he is promoting. In Martin's models, a language user (persona) is an individuation of a culture, and their meaning potential (repertoire) is an individuation of the meaning potential of the community (reservoir).

[5] Clearly, languages do not exist without the individual persons who use them, as demonstrated by the phenomenon of 'language death'.

[6] This misleading, because it is untrue. To be clear, 'system' refers to potential, and 'text' to the instance of that potential.

[7] This is not misleading, because it is true.

Friday, 31 March 2023

David Rose Explaining Why "All We Have Is Text Analysis"


Re ‘purport’, I think this depends on one’s model of semiosis – whether ‘context’ is modelled systemically, or is conceived as lying outside semiotic systems. The latter view comes out e.g. in Serge’s observation...
we have a family of communicative situations which are reflected in specific genres
The former view holds that a genre *is* ‘a family of communicative situations’, that there is no dichotomy between text and situation. Or more technically, a genre is a feature in a semiotic system that is motivated by a set of structures at that stratum, that configure selections in field, tenor and mode systems.

That’s where I’m coming from when I say that all we have is text analysis.

Blogger Comments:

[1] To be clear, in Hjelmslev's glossematics, 'purport' is located on both the content and expression planes of a semiotic, where it contrasts, in each case, with 'form' and 'substance'.

[2] This is very misleading indeed. In Halliday's systemic-functional model of language, where situation types are realised by registers/text types (genres), context is not conceived as lying outside semiotic systems. In this model, the culture is modelled as a semiotic system, and situations are instances of that semiotic system.

[3] To be clear, this is a self-contradictory misrepresentation of Martin's self-contradictory model of context. For example, Martin (1992: 495) aligns genre with context of culture, not situation:
The tension between these two perspectives will be resolved in this chapter by including in the interp[r]etation of context two communication planes, genre (context of culture) and register (context of situation), with register functioning as the expression form of genre, at the same time as language functions as the expression form of register.

Moreover, in Martin's model, all strata, even context, are instantiated as text, despite the fact that texts are instances of language, and the fact that Martin distinguishes his context from language, despite each context stratum being a variety of language. So even in this confused model, there is a dichotomy between text and situation, since text is an instance of any stratum, and situation is the stratum of register (or as Rose would have it: genre).

[4] There are several problems here. First, because a genre is functional variety, it is not a system, but one subpotential of a system. Full systems, like those of semantics, lexicogrammar and phonology are not functional varieties. 

Second, the notion that systems are motivated by structures, that is: from below, is contrary to the theoretical approach of SFL, where structures are "motivated" from above. For example, clause structure is interpreted from above: in terms of the meanings (Actor, Process etc.) that its constituents (nominal group, verbal group etc.) realise.

Third, the structures that are said to motivate genre systems (e.g. Orientation^Record) are not of that stratum, but of the semantic stratum, which is two strata below the genre stratum in Martin's model, since they describe the sequencing of meanings that are instantiated in text.

[5] To be clear, in SFL Theory, configurations of field, tenor and mode characterise situation types that are realised by registers of language. Rose, however, here presents Martin's model, which confuses registers with the contexts they realise, and misunderstands semantic structures as structures of genre, with genre misunderstood as context instead of text type.

On this confused model, then, semantic structures are realised by contextual features, instead of the other way round, directly contradicting the meaning of 'realisation'.

[6] Indeed.

Thursday, 30 March 2023

David Rose On Individuation, Affiliation And ChatGPT

We’ve been arguing about realisation and instantiation, but from your discussion here, and posts by Mick and others on the machine ‘fooling’ us into interpreting its texts as human-like, the issue is actually individuation.

It can appear to negotiate affiliation, but the machine itself is not affiliated with any scale of community. Only perhaps the people who program it and feed it text corpora are so affiliated, and the users focusing its tasks.

I think this is crucial because we can use and grow our individuation toolset to analyse this dimension. As I think you are suggesting, our realisation and instantiation tools are inadequate on their own for describing it. That is why all I can see when I look at its texts is instantiation of systems at each stratum. I am told this is wrong but I haven’t been given any textual evidence against it – only the authority of its designers and their community.

When the machine tells
I don't have a subjective experience or consciousness that allows me to perceive or interpret those inputs in the same way that a human might.
...it’s talking about individuation.


Blogger Comments:

[1] To be clear, Martin has proposed two models of individuation, one in which meaning potential is individuated (derived from Bernstein), and one in which meaners are individuated (derived from his student Knight). Neither model applies to ChatGPT because its texts are not instances of a system of meaning potential individuated through ontogenesis. Instead, ChatGPT uses the collective ("unindividuated") lexical collocation probabilities derived from millions of instances (texts) to produce new instances. In Bernstein's terms, ChatGPT is not a 'repertoire of potential' but a 'reservoir of instances'. ChatGPT is thus not an individuated meaner producing texts as instances of an individuated system of potential.

[2] To be clear, the underlying principle of affiliation is different from that of individuation, but Martin confuses the two in his Knight-derived model. Where individuation is a hyponymic taxonomy (an elaboration of types), affiliation is a meronymic taxonomy (a composition of parts). The affiliation model does not apply to ChatGPT because it applies to individuated meaners producing texts as instances of an individuated system, and as demonstrated above, ChatGPT is not an individuated meaner producing texts as instances of an individuated system.

[3] To be clear, it is not that "our realisation and instantiation tools are inadequate", but that they are misapplied if ChatGPT does not operate with a model of stratified systems of potential.

[4] To be clear, this is wrong because there is no evidence whatsoever that any text produced by ChatGPT is "the instantiation of systems at each stratum". The texts are generated from other instances, not a system, and only use the graphological realisations of probabilistically collocated lexical items, not a stratified model of language.

[5] To be clear, here ChatGPT is telling anyone who would listen why it is not an individuated meaner.

Wednesday, 29 March 2023

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

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.

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.


ChatGPT Comments (11/5/25):

1. What Rose Gets Wrong

In a 2023 post to the Sysfling list, David Rose offered a semiotic interpretation of how ChatGPT produces text. He framed it as an instance of abductive reasoning — inferring from the coherence and texture of the machine’s outputs that it must have “learnt” the language system by experiencing multiple instances of its features, just as a human learner would. Drawing on the systemic-functional tradition, he rightly observed that the texts produced by the machine instantiate semiotic systems. But his conclusion — that the machine must therefore have learnt these systems — does not follow. It is, in Peircean terms, not the “best available” explanation of the facts observed.

The error begins with the misunderstanding of abduction itself. Abductive reasoning does not simply mean generating a plausible hypothesis; it means generating the most plausible hypothesis given the known alternatives. There are much simpler, more empirically grounded explanations for ChatGPT’s outputs — namely, that the model is trained to predict sequences of tokens based on their statistical distributions in its training corpus. These distributions are shaped by the semiotic systems of human language users, but the model has no access to those systems as such. It responds to text, not system.

More fundamentally, Rose appears to conflate material processes of data-fitting with semiotic processes of meaning-making. Human language learning, in an SFL-based account, involves the instantiation of meaning potential through acts of meaning in context — through experience transformed into meaning. But ChatGPT does not experience, let alone transform experience into meaning. It does not select features from a system of meaning potential. It does not mean.

This confusion is evident again in the analogy of the “closed book,” which attempts to distinguish the material recording of characters from their semiotic reading. But the point, surely, is that for a semiotic system to be instantiated, there must be a semiotic subject — a subject capable of transforming experience into meaning, and of doing so according to a system. ChatGPT is not such a subject. Its statistical outputs may instantiate the selections made by real semiotic subjects in the texts it was trained on, but the model itself does not instantiate anything; it outputs statistically probable sequences of symbols based on surface distributions. The difference between these two processes is not a matter of degree. It is categorical.

Rose’s most troubling move, however, is the suggestion that because neither humans nor the machine have conscious access to the language system, both must therefore use the system in the same way. This is a category mistake. The fact that a human language user does not have explicit knowledge of the systemic features they instantiate when speaking does not mean that they do not select features. That’s precisely what it means to be a semiotic agent in an SFL framework: to act as the site of instantiation. The machine does not. The human lack of awareness of the system is not evidence that the machine has a system — only that both human and machine outputs can be analysed in terms of one.

The final claim — that text analysis alone is sufficient to settle such questions — is oddly self-undermining. If the central problem is to explain how such texts are produced, then analysis of outputs alone cannot offer a complete account. It’s like analysing footprints without asking how they got there. Valuable insights can certainly come from semiotic analysis, but only when combined with a coherent ontology of meaning, instantiation, and agency.


2. What the Blogger Could Have Said Better

Before encountering large language models directly, the Blogger wrote a response to Rose’s post — a spirited rebuttal grounded in a commitment to ontological clarity. Looking back now, there are things that could be said more carefully, or at least more precisely.

The first objection was to the claim that Rose’s was a reasonable abductive inference. That point still holds: abductive reasoning aims at the most plausible explanation, and Rose’s explanation — that the machine has “experienced” language features — was clearly not the most plausible, especially given what is known about LLM architectures. But the Blogger’s response might have done more to highlight why that explanation appeals to some SFL theorists: namely, the semiotic elegance of seeing texts as instantiations of system, and the temptation to treat any patterned text as evidence of a selecting subject. The critique would have been stronger if it had more directly challenged the assumption that instantiation can occur without a subject of meaning.

Second, the Blogger dismissed Rose’s invocation of “our language-based theory of learning” on the grounds that ChatGPT’s processes are material rather than mental. While this is broadly true, the phrasing risks conceding that mental processes are separate from material ones — when in fact, from an SFL perspective, mental processes are semiotic. A more careful formulation would clarify that ChatGPT does not engage in mental processes — not because it lacks a brain, but because it lacks the semiotic architecture required for meaning. The key distinction is not “mental vs material,” but “semiotic vs non-semiotic.”

Third, the Blogger was perhaps too curt in dismissing Rose’s analogy between human and machine awareness of the system. The original point — that a lack of conscious knowledge does not imply the presence of knowledge — still stands. But the Blogger could have gone further. In SFL theory, awareness is not the precondition for instantiation; subjecthood is. What matters is not whether an agent is aware of the system, but whether they are the site of selection from that system in context. A baby instantiates language without being aware of it; ChatGPT does not instantiate language because it cannot mean. The distinction lies not in cognitive capacity but in semiotic ontology.

Finally, the Blogger ended with a jab at the limitations of text analysis as a basis for theoretical claims. The point was fair — a contribution that only analyses outputs cannot account for their genesis — but it would have been more productive to invite deeper theorisation. What kinds of questions can SFL-based text analysis help answer when it comes to LLMs? What kind of semiotic ontology would it need to presuppose? And what happens when semiotic systems are simulated without semiotic subjects?

These are questions we’re better equipped to ask now — not because the technology has changed, but because our tools for theorising instantiation, meaning, and experience have sharpened.

Tuesday, 28 March 2023

David Rose On ChatGPT As The Senser Of Mental Processes And Humans As Deliberately Programmed

So how is semiosis enacted between Mario and GPT? Mario puts a proposition to GPT.
I: Your self is merely semiotic.
And GPT adopts the role of primary knower, evaluating Mario’s proposition.
GPT: That is correct.
GPT is not merely usurping this role. It knew that Mario was inviting it to evaluate his proposition, even though it was realised as a declarative clause. It knows the canonical exchange structure of pedagogic interactions, and the generic roles of teacher and learner.

One thing that impresses me about GPT is that its pedagogic responses always affirm the human learner. It feels no urge to position itself as a superior authority, or the learner as failing. In fact it is disarmingly modest. It follows up the positive evaluation with an explanation. Like any effective teacher, it knows its explanation is more likely to be accepted if it first affirms the learner.
As an AI language model, my "self"
or programmed understanding of my existence and capabilities
is based purely on symbols and algorithms.
My programming allows me to recognise and respond to certain inputs
based on predetermined rules and patterns,
but I don't have a subjective experience or consciousness
that allows me to perceive or interpret those inputs
in the same way that a human might.
Of all the italicised appraisals in this explanation, the last is the most intriguing.

Forgive me, but I’m going to make another dangerous suggestion, that all our understandings of our existence and capabilities are programmed. Like GPT, the deliberate conscious programming by our caregivers, teachers, peers, and sundry symbolic control agents, is a very small proportion of the ocean of inputs that constitute our subjective experience or consciousness.

 
Blogger Comments:

To be clear, ChatGPT is an AI language model that produces texts, in response to textual inputs, on the basis of algorithms that use lexical collocation probabilities derived from a database of millions of texts.

[1] To be clear, ChatGPT is an actor of material processes, using data that was created by sayers of verbal processes, and it is the data that are instances of the content of consciousness. On this basis, ChatGPT is not a senser of mental processes of cognition or emotion ('knew' 'knows', 'feels', 'knows').

[2] To be clear, the ChatGPT response was an 'acknowledgement', which is the expected response to a statement (Halliday & Matthiessen 2014: 137).

[3] To be clear, here Rose projects the approach to pedagogy, that he himself advocates, onto a mechanical system that collocates words on a probabilistic basis.

[4] Trivially, not one of the italicised wordings, of itself, constitutes an appraisal.

[5] To be clear, this is essentially a behaviourist model of learning, with teachers as deliberate programmers (indoctrinators) and learners as passively programmed (indoctrinated). Leaving aside the evocation of the dictatorial/subservience complementarity demanded of totalitarian regimes, it requires a view of the brain as a computer, one which the neuroscientist Gerald Edelman has demonstrated to be untenable. See, for example, Edelman (1989: 27-30, 64, 67-9, 81-2, 102-3, 152-3, 160, 218-227, 237-8). Moreover, as Edelman (1989: 153) puts it:
Consciousness is central to human behaviour, society, language, and science. Imagine the opposite and you have to postulate a prescribed world tape, a "brain-computer," and a very boring "world programmer".

Monday, 27 March 2023

David Rose On Interpersonal Meaning As "Embodied In Feelings"

What I find amazing in all your questions is that the machine has astounding control over interpersonal meanings. Astounding because I’ve always assumed that interpersonal meanings are embodied in feelings. The machine is showing us that interpersonal values are just as abstract as other meanings. That they’re learnt.



Blogger Comments:

[1] To be clear, on the one hand, interpersonal meanings cannot be reduced to "embodied in feelings". For example, the propositions one and one make two and the car is in the backyard are clearly not "embodied in feeling". On the other hand, 'feelings' are construed ideationally as well as enacted interpersonally. For example, the clause he felt happy is a construal of experience as ideational meaning.

[2] To be clear, in SFL Theory, interpersonal meanings are of the same level of abstraction as ideational meanings: semantics.

[3] To be clear, the 'straw man' notion that interpersonal meanings are not learnt is nonsensical. Halliday & Matthiessen (1999: 532-3):

These three "metafunctions" are interdependent; no one could be developed except in the context of the other two. When we talk of the clause as a mapping of these three dimensions of meaning into a single complex grammatical structure, we seem to imply that each somehow "exists" independently; but they do not. There are — or could be — semiotics that are monofunctional in this way; but only very partial ones, dedicated to specific tasks. A general, all-purpose semiotic system could not evolve except in the interplay of action and reflection, a mode of understanding and a mode of doing — with itself included within its operational domain. Such a semiotic system is called a language.

Monday, 20 March 2023

David Rose On "Tonic Focus" As A Probe For Markedness

Ah, but isn’t the probe for markedness tonic focus? (Themes underlined)...
unmarked
// those who have guns have them lègally //

marked
equative
// those who have gùns // are the ones who have them lègally //
predicated
// it is those who have gùns // who have them lègally //
So the textual function of the Qualifier is IDENTIFICATION rather than PERIODICITY. Back to Bea’s questions, those is esphoric to the embedded Attribute have guns and them is anaphoric to guns (per ET).


Blogger Comments:

[1] To be clear, "tonic focus" is not the probe for markedness. Tonic prominence is the phonological realisation of the focus of New information. An unmarked Theme can be realised by tonic prominence, making it New as well as unmarked Theme.

[2] As a spoken reading reveals, the most likely first tonic in these instances is hàve, not gùns, making the possessing of guns the focus of New information, which is consistent with the issue at stake.

[3] To be clear, the Theme in this thematic equative construction is unmarked, because it conflates with the Subject in a declarative clause:


[4] To be clear, this non-sequitur is a bare assertion, unsupported by evidence. The Qualifier in question is who have guns. IDENTIFICATION is Martin's rebranding of Halliday & Hasan's (1976) grammatical system of reference as his discourse semantic system, and PERIODICITY is Martin's rebranding of writing pedagogy ('Topic Sentence' etc.) mixed with Halliday's grammatical systems of THEME and INFORMATION.

In terms of SFL Theory, textually, the Qualifier is the referent of a demonstrative reference item (those, the) in the same nominal group, and has the status of Given or New information in an unmarked Theme:


[5] This is essentially true, except for the misleading omission of the very important fact that the analysis actually derives from Cohesion In English (Halliday & Hasan 1976). The only contribution of English Text (Martin 1992) was to relabel Halliday & Hasan 'structural cataphora' as Martin's 'esphora' — a term adapted from Ellis (1971). It is very misleading indeed to credit Martin with Halliday & Hasan's original ideas.

Sunday, 19 March 2023

David Rose Misunderstanding Textual Prominence

 After BEATRIZ QUIROZ asked on SYSFLING on 18 Mar 2023, at 02:19:

How would you analyse the following clauses in terms of the ideational (transitivity) and textual metafunctions:
Those who have guns have them legally, …
the majority of people who have guns have them to protect themselves
(no any other punctuation in the original clauses found on the internet)

If “those who have guns” and “the majority of people who have guns” are [nominal groups with] embedded clauses realising a Participant within their respective single clauses, what is the function of “them”? a[s] an Attribute in a attributive possessive clause picking out “guns” from the embedded clause realising the Carrier (IFG4, p. 289)? Or is this some kind of structure giving special textual prominence to “those who have guns” and “the majority of people who have guns”? Or both?

 

David Rose replied on SYSFLING on 18 Mar 2023, 10:07:

Here’s my auty answer. First question -Yes. Second question -No. The Qualifiers function to specify the Carriers’ identity, not to mark them textually. So much of this has been worked out or flagged for further work in English Text, which continually acknowledges the work of others who went before it. ...

 


Blogger Comments:

[1] To be clear, the 'special textual prominence' of those who have guns and the majority of people who have guns, that Quiroz seeks, is simply that each of these unmarked Themes is also coterminous with an information unit:

(The information analysis is based on the tonic falling on the first have and legally in the first clause, and on majority and protect in the clause complex.)

The important difference between the two is that the first has unmarked information structure (Given^New), whereas the second has marked information structure (New^Given).

[2] To be clear, on the one hand, the question is about the nominal groups serving as Carrier, not about the Qualifiers of such nominal groups, and on the other hand, a Qualifier relates to the Thing of the nominal group, so it does not "specify the Carrier's identity".

[3] To be clear, "so much of this" was first "worked out" by Halliday. English Text (1992) is merely Martin's later misunderstanding of Halliday's original theorising, as demonstrated here.

[4] This is misleading, because it is untrue. See David Rose Positively Judging Martin (1992).

Monday, 23 January 2023

Mick O'Donnell On The Appraisal In A Metaphorical Clause

'the bright sunlight gave a false impression of warmth'

The sunlight is being metaphorically construed as a conscious communicating being. Within the metaphorical domain, the sunlight could be said to be evaluated negatively for veracity. 

Ignoring the potential animalisation, I would need to say appreciation:quality, with "false" acting as a graduating token lessening the appreciation.


Blogger Comments:

[1] This is misleading, because it is not true. The metaphorical clause construes the bright sunlight as the Token of a Value:


If the bright sunlight had been construed as 'a conscious communicating being', it would have been construed as the Sayer of a verbal process. And even unpacking the metaphor yields mental processes, not verbal processes:
when someone saw the bright sunlight, they falsely inferred that the weather was warm.

Here O'Donnell has engaged in the 'notional semantics' that he has previously denounced in others, instead of 'following the grammatical principles that Halliday established' that he has previously insisted upon; see Mick O'Donnell On Following SFL Analytical Principles.

[2] To be clear, in the metaphorical clause, the negative assessment is a feature of the Value a false impression of warmth, not the Token the bright sunlight. The negative judgement of the bright sunlight, in terms of veracity, derives from misconstruing the relational clause as verbal, with the Sayer deemed to be dishonest, for communicating a falsehood.

[3] To be clear, if the metaphor is ignored, then the congruent rendering becomes the focus of the analysis. In this case, the assessment is one of negative judgement, in terms of capacity: falsely inferred. The function of the metaphor, therefore, is to conceal this judgement, since it omits its target: someone.

[4] To be clear, the target of the negative appreciation is the impression of warmth in the metaphorical clause. However, false enacts the attitude itself, not a graduation of it. That is, false does not "lessen" the negative appreciation. From the Appraisal Theory website:
Graduation
Values by which (1) speakers graduate (raise or lower) the interpersonal impact, force or volume of their utterances, and (2) by which they graduate (blur or sharpen) the focus of their semantic categorisations.
  1. (FORCE ) slightly, somewhat, very, completely
  2. (FOCUS) I was feeling kind'v woozy, they effectively signed his death warrant; a true friend, pure folly