# Theoretical perspectives on the mechanisms of 'AI' chatbots *July 2025* #final #AI #LLM #Theory AI chatbots and the Large Language Models (LLMs) that underpin them seemingly hold an abst representation of 'knowledge'. As their name states, they only model language, yet they certainly have *procedural* knowledge in that they *know how* to output text that, had it been authored by a human, would be a sign of knowledge. To investigate the sources of this apparent knowledge, this essay offers a brief overview of the technical mechanisms of language modelling in LLMs, as section headings. Presented alongside in sub-sections are several theoretical perspectives that help contextualise chatbots' relationship to meaning, their internal representation of it, and eventual output. To frame the first technical explanation and ease the reader in, we fist need to briefly talk of structuralist linguistics. # Prelude: Linguistic Structuralism Forster (2022) describes LLMs as "operationali\[s\]ation of Saussurean structure". The Swiss linguist Ferdinand de Saussure (1857-1913) birthed linguistic structuralism, which conceptualises language as a *structure of signs*. Signs consists of pairs of *signifiers* (words, written or spoken) and *signifieds* (the concept referred to). Important in this conception is that signified are not real world object/entities - they are abstractions, once removed from the thing they describe/capture. Signifiers, further removed from the real world, are *arbitrary* (de Saussure, 1989 \[1916\]). Or inded *largely* arbitrary: an example of a notable exception is the "bouba/kiki" effect whereby people across cultures will associate the former word to a cloud-like, round shape, the second to an urchin-like, pointy one (Ramachandran and Hubbard, 2001; Ćwiek _et al._, 2021) > ![[Kiki-Boboo.png]] > In Ramachandran and Hubbard's cross-cultural reproduction of Wolfgang Köhler's original experiment, 95 to 98% of participants chose 'kiki' for the left shape, "bouba" for the right one. Signifiers share *syntagmatic* relationships: syntactical and semantic ones; conversely, signifieds share *paradigmatic* relationships. It is those sets of relationships that form the *structure* of language, which exists in the space of signifieds as it does that of signifiers. This structure is *differential*: meaning is in the relationships between signifiers on the one side, and signified, on the other, more than it is in the relationship between a notional, purely abstract signified and an arbitrary signifier (de Saussure, 1989 \[1916\]; Inglis and Thorpe, 2012). For instance the signifier 'cat' does not refer to the actual animal, but to a signified encompassing the concept of the domestic cat, the big cat, and the 1930s jazz lingo for a hip fellow, along with the connotations that underpin this particular use: graceful movement, fierce independence, quiet, understated *cool*. Development, in the last fifteen years, of deep learning techniques for Natural Language Processing implement Saussure's theory, inasmuch as they have shown to model paradigmatic relationships whilst having only encountered syntagmatic ones in their training (Forster, 2022, Vromen 2024). # In the beginning was the *word2vec* Artificial neural networks, like the ones powering LLMs, model a set of 'neurones', each receiving weighed inputs from all the other neurones of a previous *layer*, using linear algebra. Each layer is a *vector*, a column of numbers, each of which represent the degree of activation of a neurone. The strengths of the synaptic connections between neighbouring layers is a *matrix* of *weights*, transforming the vector representation of a layer into that of the next (Zou, Han and So, 2009). This mathematical detour is to stress that LLMs manipulate *vector representations of words.* To be precise, they use vector representations of *tokens*; a token can be a word, part of a word, as well as punctuation and control characters. Any vector, being just a list of numbers, can be thought of as a set of coordinates: eg. '(x, y)', or '(x, y, z)'. Those vector representations, called '*word embedding*s', can in turn be thought of as *coordinates in a hyper-dimensional space.* For GPT-3, the LLM underpinning the original ChatGPT, this space has 12,288 dimensions (Brown _et al._, 2020). The *dimensions* of this hyper-dimensional space are arbitrary; the number of dimensions is chosen before training, but what each will end up representing is arbitrary: many models will have the same version available in a variety of parameter sizes. In spite of this, with enough dimensions to this 'latent space', it will end up modelling some aspect of the meaning of the words as they appear in the training set. The oft repeated example is that, in those embeddings: $vec(queen) - vec(woman) \approx vec(king) -vec(man)$ Here, $vec(king) - vec(man)$, also a vector, representing a direction, a motion in the latent meaning space, that turns an individual into a monarch - it is the same quantity as $vec(queen) - vec(woman)$ (Mikolov, Chen, _et al._, 2013). The technique of word embedding pre-dates the transformer architecture that powers GPT-like chatbots (see Mikolov, Sutskever, _et al._, 2013), and has been used for machine translation since the 2010s. Creating those embeddings is a separate training stage, in which the system is optimised for its ability to guess missing words within a sequence. The most surprising thing is how short the sequence can be for embedding to model such semantic relationships. The 'regal equality' above is already present in spaced modelled with `word2vec`, using sequences *no longer than 9 words* (Mikolov, Chen, _et al._, 2013; Mikolov, Sutskever, *et al.*, 2013). ## Analysis: Meaning as distribution / meaning as intent Embedding is thus specifically an operationalisation of *distributionialism*, a late-twentieth century structuralist theory of linguistics (Harris, 1951, 1954; Bloomfield, 1984), best summed up by J. R. Firth: "You shall know a word by the company it keeps !" (Firth, 1957, p. 11). At its core is the hypothesis that the semantics of language - the structure of meaning - can be completely described in terms of a distributional "without intrusion of other features such as history or meaning" (Harris, 1954, p. 146). Harris gave little consideration as to whether history or meaning may 'intrude' on distributional statistics in the first place, and we will find that the linguistics of LLMs share this disregard for ground truths or communicative intent as the *origin* of linguistic behaviour, in favour of a conception of meaning solely based on the *outcome* of linguistic behaviour: the statistical properties of text. Saussure made a further distinction between the *langue*, language as the complete sets of shared signs, and *parole*, language as it is spoken or written, by specific people, in a specific context (de Saussure, 1989 \[1916\]; Inglis and Thorpe, 2012). The training data of LLM is *parole*, so is their output - can they model *langue*? Then again, could not the same questions be asked of humans? LLMs model *standing* (or *conventional*) meaning (Grice, 1968; Quine, 2013) , but not meaning as communicative intent. In French, 'to mean' is '*vouloir dire*' - literally to want to say - and this is the phrase used not just for 'I mean', but also 'this word means'; 'mean*ing*', conversely, is a separate noun: '*sens*' - obviously as in 'sense', but, coincidentally, the word used for the *direction of a vector*. LLMs represent the *sens* (meaning), but not what the words *want to say* (Bender and Koller, 2020). # The Transformer Architecture After word embeddings, another turning point in the history of language models is the development of the transformer architecture (Vaswani _et al._, 2017). Like predictive texting, all an LLM does is *guess* the next word based on those input so far - the *context*. The process of generating the next token is thus known as *inference*. When simple predictors would use blunt techniques, only considering the immediately preceding word and/or the full set of previous words, transformers use several 'attention heads', which let them consider context, in different places in the input. Each of those heads perform a series of transformations on the vectors, the matrix multiplications mentioned above, that will shift their position in the embedding space, refining their meaning. The mechanism is known as 'self-attention': each of the computed vectors will be weighted, multiplied by a scalar reflecting its relevance in the context - as pertains guessing the next token. This is repeated several times; GPT3 uses 96 transformer blocks operating sequentially, with the output of each block (a sequence of vectors representing the context) becoming the new context input to the next one. If the original embeddings represent standing meaning, what comes out of the transformer, and ultimately drives the choice of the next word, represent meaning-in-context (Vaswani *et al.*, 2017; Zhang* _et al._, 2025). This last step makes the representation of meaning during the inference a dynamic, context-dependent one, as opposed to the standing meaning of the original embedding. Both these representations of meaning are based on the distributional semantics of the training data, as they have been trained by minimising a prediction error in a missing-word exercise. Embedding and generator training are two distinct steps which may or may not use the same training data, but the system cannot model meaning beyond that present in the training set(s). When a model generates a vector for the next token, it represent a point in the embedding space that will never be the exact location of a word, falling somewhere in between. Candidate words are found nearby, ranked by proximity, and the result is drawn at random, with probabilities weighted according to this distance (Vaswani _et al._, 2017). ## Analysis: The post-structural linguistics of transformers The great challenge of structuralist linguistics to modernity was to posit that the structure of language itself, in the syntagmatic space, precludes, limits, enables or favours our ability to conceive of objects in the paradigmatic space - an hypothesis most associated with the *linguistic relativism* of Sapir and Whorf in the 1920s (Gerrig and Banaji, 1994). The impact of the training data on bias in the output of LLMs is a reflection of this tenet (Vromen, 2024). The vector for 'cat' will not be the same in a system trained on zoology textbooks, versus one trained on the correspondence of jazz musicians. Post-structuralism offers a further challenge, casting doubt on the very existence of the paradigmatic space, or at any rate of a *shared* paradigmatic space: meaning stems from language and its use in the social (if not embodied) world, and diffuses through usage, constantly negotiated, creating its own network of concepts and relationships for a given reader (Grbich, 2003; Aylesworth, 2015). Roland Barthes, whose career moved through structuralism into post-structuralism famously argued for the irrelevance of the author's intention once a work is published (1967). The reader is free to connect the text to a vast, open network of other texts and cultural codes. He distinguished between the closed 'Work' (which can be analysed structurally) and the open 'Text'. The Text, he wrote, "is a methodological field," not an object (Barthes, 1989). Manghani (2024) remarks that the training method of language model is remarkably similar to Barthes "commutation test" (Barthes, 1990 \[1967\]); and argues against Barthes claim of the Text's non-computability. This argument, in my view, is only valid for the platonic ideal of the LLM, trained on all human text (including that yet to exist); it is a philosophical position that brackets the reality of model training. Meaning emerges not solely from difference, but also from *différance*, the differential in local meaning between the times and cultures of the writing and the reading - Derrida wrote a lot on the technology of writing, and must have known he would challenge spell-checkers of future scholars as much as he did Saussurean linguistics. Derrida's view of the primacy of writing - to Saussure primacy of speech, means that there is '*nothing but the text*': intent is moot, meaning constructed on the reader's side (Derrida, 1967; Lawlor, 2023). In this respect, the LLM realises Derrida's vision: its production of text is devoid of intent, and whilst it ostensibly shows, in its output, a grasp of 'meaning', it is through a set of mathematical abstractions so alien to human thought that it forces us to acknowledge it is the reader that constructs the meaning of the synthetic text (Kuchtová, 2024; AlShalan, 2025). # Fine-tuning After vector embedding by the encoder, and the training of the parameters of the generator, the transformer is only *pre-* trained - the P in GPT. The model is then *fine-tuned* for specific applications. The *base model*, as it comes out of this pre-training process, is *in potentia* capable of all the applications its fine-tuned variants, especially if given enough context. In practice, they are further trained, on the same token prediction task, using a different, more specialised but smaller, dataset - which is still, as all the above, *unsupervised* learning. In addition to this "pre-train and fine-tune" approach, there is the "pre-train, prompt and predict" one, in which models undergo *reinforcement learning*, this time completing full prompt->response tasks and being given feedback. This typically involves the writing of specific example responses to thorough, comprehensive prompts, then using humans to rate similarity of output with the exemplars (Cheng _et al._, 2023). This now more and more automated, making this amongst the first of those lowest-level knowledge work jobs to be lost to the LLM (Mazzullo _et al._, 2025). Fine-tuning is application specific, which in practice makes the nature and details of it closer to a trade secret: the software industry has a long history of building successful proprietary systems on top of open source infrastructure. Some systems are fully proprietary, others 'open-weights', with all parameters published, available for further tuning. A public set of parameter is open source in letter, allowing anyone to run and adapt the model, but part of the spirit of open source is transparency by code inspection. To this effect, some models go further and offer a full pedigree - the training data and details of methods used (Widder, Whittaker and West, 2024). ChatGPT is at the least transparent end of the spectrum, OpenAI's own name notwithstanding (Liesenfeld, Lopez and Dingemanse, 2023). Yet we know that the numerous lexical fingerprints ('delve', 'tapestry'... see Kobak _et al._, 2025) of its output do not come from the training set of the underlying GPT base model, but from the fine-tuning. The verb 'delve' in particular has been picked up on, being relatively rare in the dialects of first-world English speaking countries (\[@JeremyNguyenPhD\], 2024). It is, however, heavily used in Nigerian business English: a marker of the off-shoring to the global South of the human labour needs of reinforcement learning (Hern, 2024), where OpenAI "paid people \[...\] $2 an hour to look at the most disturbing content imaginable" (Harrison Dupré, 2022). ## Analysis: bovine scatology Their functional mechanism means that AI chatbots' output relationship to ground truth is merely statistical: curation of the training set and fine-tuning help increase the likelihood of truthful output past an acceptable threshold (Bender _et al._, 2021). The reinforcement learning from human feedback used in the same fine-tuning process will also align the model towards output more likely to be positively rated by a human. This dangerous combination has led many to invoke Frankfurt's *Bullshit* (1986/2005): neither truth nor lie, having no regard for either; an instrumentalised language whose sole purpose is a specific effect on the reader (qv. Rudolph *et al.* (2023), Hicks *et al.* (2024) or Gorrieri (2024)). # The System Prompt We have seen that the very architecture of transformer models makes their performance directly proportional to the amount of context they are given. The more context, the more opportunity for the attention heads to transform the position of the word vectors in the latent space, towards their meaning-in-context. Fine tuning helps lock in this context-specificity; the base model of any LLM is trained to continue text, not to respond conversationally: to act as chatbots, LLMs need fine-tuned to this specific behaviour. In addition to this, the whole conversation also has to be given a frame by the bot developers - the system prompt. System prompts are as sensitive a trade secret as details of fine-tuning, but they are often extracted from the bot by inquisitive users, and leak online (Levin _et al._, 2025). When I type a prompt into ChatGPT, my dozen-word string will be appended to a thousand-plus words (for ChatGPT 4.5 see Appendix **X**), presented with subheadings and bullet-pointed list (which explains their prevalence in outputs). LLM output being only as good as the amount and quality of context given, it is easy to see how this biases the output (Neumann _et al._, 2025). The system prompt contains phrases like *"Always prioritize being truthful, nuanced, insightful, and efficient, tailoring your responses specifically to the user’s needs and preferences."* (quoted in u/EloquentPickle, 2025), reminiscent of those tales of conflicting AI instructions, core to the plot of many of Isaac Asimov's *Robot(s)* stories (1996 \[1950\]). ## Analysis: The chatbot as a tool of discursive Power Fine-tuning and system prompt are where the chatbot developers have the most influence in its eventual behaviour. With only half a dozen companies competing in this field, and OpenAI maintaining a steady ~80% market share (StatCounter, 2025), this concentrate in very few hands an enormous power: that of discourse production. Exact figures are hard to obtain, but ChatGPT alone was reported serving more than 1 billion queries a day as of June 2025 (Singh, 2025). Assuming an estimate of 50-150 words per query, that is 50-150 billion words output a day. To put this in perspective, the whole of Wikipedia as of early July 2025 totalled 4.9 billion words (Wikipedia, 2025). *ChatGPT prints between ten and thirty wikipedias every day* - to 122.5 million users, with an over-representation of 18-34, males and Americans (Singh, 2025). The discourse produced by ChatGPT reflects, expectedly, that of its training data, describing for instance communism as an ideology and economic system, but capitalism merely as an economic system (Ahmed and Mahmood, 2024). This is not surprising, and mirrors legacy media discourse. What is unsaid is that Ahmed and Mahmood were not exposed to the content the $2 an hour labour (Perrigo 2023). Chatbot designers have to make a decision as to what discourse is acceptable. In addition to their reach, chatbots are, to some, very influencial. AI researcher forewarned of the risks of LLMs as tools of radicalisation (McGuffie and Newhouse, 2020), a risk now realised (Allchorn, 2024), but they are also tools for de-radicalisation (Russo, 2024). # Final thoughts > "Why are we using a Bullshit engine for *anything serious*? Like *forreal-forreal*?" > (_Signal’s Meredith Whittaker says Chat GPT can’t be trusted_, 2023) The mechanics of LLMs make it hard to argue they 'know' anything. Factually correct output is contingent on a deterministic stage so complex as to be mathematically *chaotic*, with small change in initial conditions (different wordings of the same query) can result in large, unpredictable changes in output, followed by a probabilistic roll of the dice to pick the exact word. This is not noticeable for many inputs, where the fuzziness of human language will allow for interpretation of the output as correct; however, it is blatant when asking an LLM to do maths, where each digit in the output needs to be correct, not just semantically close enough. See also ChatGPT's persistent inability to tell how many r's are in the word *strawberry*: vector embedding does not encode the tokens' spelling, the answer is based on statistical modelling of the training set as regards phrases containing those tokens. 'Stochastic Parrots' indeed (Bender *et al.*, 2021). Thus, LLMs have no *declarative* knowledge, but we can grant them *procedural* knowledge: knowing how to sequence output tokens in a fashion likely to be interpreted as meaningful output by a human user. This 'meaning', we have seen, comes from four factors. Embeddings define the base possibilities through static meaning, and implement in vector mathematics the space of syntagmatic relationships of signifiers as per Saussure. The training and inference mechanisms of the generator use a numerical abstraction of the information contained in the training set. This means a more refined, contextual grasp of meaning, as derived without access to real-world referent, nor ground truths - an irrelevance of the *hors-texte* perfectly Derridean. The base models undergoes further fine tuning, first for safety, the enforcement of an equally perfectly Foucauldian regime of truth, defining the acceptable Knowledge to be output, wielding Power upon the user as Subject. Further training for conversation will reinforce output rated positively by the user, which, combined with the lack of truth grounding, makes said output Frankfurtian *bullshit*. *Papañca*, the Buddhist concept of 'mental proliferation' may however be more useful (Costello, 2024). It turns out Gautama Buddha was a post-structuralist twenty-five centuries before it was trendy. All those factors define the bounds of what is possible, with fine-tuning aiming to set those bounds around a safe area. But as to actual output, the architecture of the transformer model means the largest influence is the context: the conversation itself. As models support larger context windows, chatbots can have longer exchanges; as those grow in length, the relative influence of the system prompt on the output decreases. This has considerable implications for LLM safety, as we have seen this year with stories of users being induced into delusion by ChatGPT (Harrison Dupré, 2025; Klee, 2025). Fine-tuning for AI 'alignment' (of output to moral values) leaves the model exposed to 'jailbreak' attacks (Wolf _et al._, 2024; Chu _et al._, 2025), amongst them the "persona attack", whereby through persistent prompting, a user alters the chatbots persona past its safe boundaries. Paradoxically, a better aligned model, which discriminates better between 'good' and 'bad' states, is therefore *easier to steer into bad states* (West and Aydin, 2025). Long conversations leading to spiritual delusions are an inadvertent persona attack, inadvertently leading the model to unsafe outputs. Researchers in psychiatry had foreseen the problem shortly after ChatGPT's release (Østergaa rd, 2023); on a more hopeful note, this potential for mental harm is also a potential for mental healing (Østergaard, 2024; Rządeczka _et al._, 2025). # References Ahmed, T.N. and Mahmood, K.A. 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