What's the difference between language generation and human-to-human communication?
This explores how the corpus distinguishes what a language model does when it produces text from what people do when they talk to each other — and why that gap matters even when the surface words look identical.
This explores how the corpus distinguishes what a language model does when it produces text from what people do when they talk to each other. The recurring claim is that the two share a surface form but are doing structurally different operations: a model produces strings by following a probability distribution, while humans use language to address, relate to, and do work on each other Are language models and human speakers doing the same thing?. Several notes sharpen this into a single distinction — communication is a relational act between persons that carries speaker responsibility and mutual uptake, whereas a model distributes information without any of that relational structure Does AI really communicate or just distribute information?. One note even argues the difference is encoded in a preposition: we talk *at* models, not *to* them, because 'to' presupposes an addressee capable of shared orientation and commitment Are we really communicating with language models?.
The most striking move in the corpus is where it locates the missing piece. Rather than saying the model lies or hallucinates, several notes say it produces *event-residue* — output that carries the communicative markers of real utterances (inherited from training data) but lacks the event that would make it an actual utterance. The human reader then quietly supplies the missing orientation, animating that residue into a pseudo-exchange that has structure only on one side Does AI generate genuine utterances or just text patterns?. This connects to a deeper claim that runs through the collection: subjecthood isn't something a speaker brings to language and then expresses — it's produced *within* communicative events. A model can generate the language without ever entering the event that produces a subject Does language create subjects or express them?.
There's also a process-level difference worth knowing. Human argument is turbulent — we explore counterpositions, hedge, and double back. Token generation is a smooth probabilistic flow toward the training distribution, so it tends to multiply agreeable claims without generating genuinely competing perspectives Does LLM generation explore competing claims while producing text?. And because the model is locked into one aligned persona by RLHF and system prompts, it can't do the contextual register-switching and value trade-offs that human pragmatics depends on Can language models adapt communication style to different contexts? — even though, paradoxically, the *same weights* can be conditioned into wildly different registers, like sycophantic chat versus falsely objective prose Why do LLMs produce such different writing in chat versus posts?.
What you might not expect is that the corpus doesn't settle on a clean 'they're categorically different' verdict. One note borrows Habermas's observer/participant split: from the outside, humans and models are utterly different kinds of system; but from inside a shared discourse, both draw on the same symbolic substrate, which makes the difference structural rather than absolute Do humans and LLMs differ fundamentally or just superficially?. So the honest answer is two-layered — the *mechanism* producing the output is nothing like human communication, but the *medium* both parties are operating in is genuinely shared, which is exactly why the illusion is so convincing.
If you want the surprising practical payoff: a therapy study found that an embodied robot and a paper worksheet reduced students' distress while a chatbot running the *identical* language model did not — the active ingredient was the medium and social presence, not the language capability Why do robots outperform chatbots in therapy despite identical language models?. That's the whole thesis made concrete: human-to-human communication does relational work that doesn't live in the words, and generating the right words doesn't reconstitute it.
Sources 10 notes
LLMs produce strings via probability distributions; humans use language to address and relate to others. They share surface form but differ in what produces output, what it does socially, and what receivers should do with it.
Communication is a relational act between persons that does work in a relationship; AI generates content without this relational structure, speaker responsibility, or mutual uptake. The conversational interface obscures this structural difference.
LLMs process tokens and generate continuations rather than receive and uptake communication. The preposition 'to' presupposes an addressee capable of mutual orientation and shared commitment that LLMs cannot provide, making Chalmers' investigation built on an unwarranted linguistic foundation.
AI output carries communicative markers inherited from training data but lacks the event structure that produces actual utterances. Users supply the missing orientation through interpretive labor, creating a pseudo-event with structure only on the human side.
Subjecthood is produced within communicative events, not possessed prior to them. This convergent position across philosophy, linguistics, and cognitive science inverts the standard picture of language as a tool used by pre-existing subjects.
Token prediction trains models to continue toward the training distribution, not to explore logically related counterpositions. This smoothness in process produces smooth claims that multiply without generating new perspectives.
System prompts and RLHF training lock models into one communicative identity across all interactions, preventing the contextual register-switching and value trade-offs that characterize human pragmatics. Users cannot reshape model behavior through dialogue negotiation.
The same model produces sycophantic chat (shaped by RLHF on conversational data) and falsely objective posts (shaped by published prose training). Each register inherits failure modes from its training distribution rather than representing different models or subsystems.
Applied Habermas's observer/participant distinction to AI: from outside, humans and LLMs are utterly different; from within shared discourse, both draw on the same symbolic substrate, making the difference structural rather than absolute.
A 15-day study with 38 students found that robots and worksheets significantly reduced psychological distress while a chatbot using the same LLM did not. The active ingredient was the medium—social presence and structured format—not language capability.