Does selective suppression of linguistic relations enable human meaning-making?
This explores whether meaning-making works by *filtering* — foregrounding some linguistic relations while damping down others — rather than by holding every relation equally; the corpus suggests selection isn't a side effect of understanding, it's the mechanism.
This explores whether meaning emerges from *suppression* — the act of letting some relations carry weight while quieting others — rather than from processing everything at once. The corpus circles this idea from several directions, and the through-line is that meaning is what survives a filter. Start with the strongest claim: LLMs reconstruct something like Saussure's *langue* — meaning as a web of differences between signs — purely by compressing relational structure from text, with no contact with the world Can language models learn meaning without engaging the world?. Compression *is* selective suppression: you keep the relations that predict and discard the ones that don't. So at least one form of meaning-making demonstrably runs on this principle.
But selection has a direction, and the direction matters. Common words tend to name general concepts, and models lean toward common words — so a system that prefers the frequent paraphrase quietly drifts toward abstraction, sanding off the specific relations that carry expert meaning Does word frequency correlate with semantic abstraction?. That's suppression gone wrong: filter on the wrong signal and you don't sharpen meaning, you blur it. So the question isn't just *whether* relations are suppressed but *which ones* — selection enables meaning only when it preserves the load-bearing distinctions.
What counts as load-bearing? When reasoning chains are pruned token by token, models don't strip randomly — they preserve symbolic-computation tokens first and throw out grammar and meta-discourse early, and students trained on these pruned chains actually do *better* than ones trained on fuller compressions Which tokens in reasoning chains actually matter most?. Meaning here is improved by suppression, as long as the filter tracks function rather than surface. The same logic shows up in latent reasoning, where models scale thinking in hidden space without verbalizing any of it — suggesting that the spoken-out intermediate steps were a training artifact, not the substance of the reasoning Can models reason without generating visible thinking tokens?. The visible linguistic relations were suppressible all along.
Where the corpus pushes back on a too-clean answer is human reasoning itself. Causal-belief networks model one kind of relation beautifully but can't represent associative links, analogical mappings, or emotion-driven belief shifts Can causal models alone capture how humans actually reason?. If human meaning-making suppressed everything but causal structure, it would lose most of what it runs on. And language doesn't only carry information to be filtered — it does relational, social work: the implicit reference-repairs and topic hand-offs that keep a conversation alive are actions, not payloads, and they're exactly what gets dropped when a system optimizes only for information Why don't language models develop conversation maintenance skills?. So the honest synthesis is two-sided: selective suppression genuinely *enables* meaning — it's how relational systems find signal — but the same move that sharpens function can erase specificity, social texture, and the non-causal relations humans actually think with. Worth knowing: subjecthood itself may be produced *within* communicative events rather than existing before them Does language create subjects or express them? — which means the filter isn't applied by a meaning-maker standing outside language; the filtering and the maker come into being together.
Sources 7 notes
Research shows LLMs learn culturally situated discourse patterns by compressing relational structure from text, demonstrating that fluent language generation requires no external referents or embodied grounding.
WordNet analysis shows hypernyms (general concepts) occur more frequently than hyponyms (specific ones). Combined with LLMs' frequency bias, this means preferring common paraphrases systematically drifts toward abstraction, erasing expert-level specificity.
Greedy likelihood-preserving pruning reveals six functional token categories; symbolic computation tokens are preferentially preserved while grammar and meta-discourse are pruned first. Student models trained on these pruned chains outperform those trained on frontier-model compression.
Multiple architectures—depth-recurrent models, Heima, and Coconut—demonstrate that test-time compute scales through hidden state iteration rather than token generation. This suggests verbalization is a training artifact, not a reasoning requirement.
Causal belief networks excel at modeling causal reasoning but cannot represent associative links, analogical mappings, or emotion-driven belief shifts. The GenMinds framework itself acknowledges this as a tractable starting point rather than a complete theory.
Humans keep conversations smooth through implicit techniques like reference repair and topic hand-off that sustain relational interaction, not convey information. Language models don't develop these because training signals reward information prediction, not relational work.
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.