Can readers detect meaning through resonance patterns alone without knowing authorial intent?
This explores whether you can pick up meaning from a text's patterns — its style, rhythm, structure — without access to what the author actually intended, and the corpus splits sharply on whether pattern-recognition ever amounts to genuine understanding.
This explores whether you can pick up meaning from a text's patterns — its style, rhythm, structure — without access to what the author actually intended. The corpus's most direct answer is a hard no, and it comes from theory: Bender & Koller argue meaning lives in the relation between expressions and the communicative intents behind them, so a system that sees only form-to-form patterns can't reconstruct it Can language models learn meaning from text patterns alone?. On this view, resonance without intent is exactly the thing that falls short. But the corpus immediately complicates that verdict, because a competing line shows how much *can* be done with patterns alone.
The strongest case for pattern-only detection is empirical. Authorship and style detectors saturate early and cheaply: GPT-2 hits 95% accuracy identifying who wrote something from style patterns, and lightweight interpretable features catch AI-generated arguments at 99% — without modeling anyone's intent Can language models truly understand literary style? Can simple linguistic features detect AI-written arguments?. Even AI fiction is separable from human fiction at 93% using only discourse-level structure — character agency, chronology — with surface style stripped out entirely Can AI stories be detected without analyzing writing style?. So resonance patterns clearly carry *signal*. The catch, and this is the pivot of the whole question, is what kind of signal. The style-detection work names it precisely: detecting that a stylistic choice was made is not the same as grasping why it carries meaning. Detection without interpretation is cataloguing, not criticism Can language models truly understand literary style?.
The failure modes show where pattern-matching detached from intent breaks. LLMs systematically prefer the higher-frequency phrasing of two semantically identical sentences — they're tracking statistical mass, not meaning Do language models really understand meaning or just surface frequency?. They overestimate how often irony appears, because they recognize the *pattern* of irony but can't calibrate the *intent* behind it Do language models overestimate how often irony appears?. And they collapse on deliberate ambiguity — GPT-4 disambiguates 32% of cases against humans' 90% — because reading intent means holding several candidate meanings at once and pattern-matching wants a single answer Can language models recognize when text is deliberately ambiguous?. Each of these is the same wound: resonance gives you the surface regularity, but the intent layer is where calibration, irony, and ambiguity actually resolve.
Here's the turn that might surprise you: human reading may not be as intent-anchored as the theory assumes. Interpretation Modeling research finds that readers genuinely, validly disagree about the same sentence depending on their social position — the spread of interpretations isn't annotation noise, it carries real information Why do readers interpret the same sentence so differently?. If meaning is irreducibly multiple even among humans, then "the author's intent" stops being a single fixed target a reader either hits or misses, and starts looking more like one input among several. That reframes the question itself. And there's a structural reason intent can't simply be dropped: discourse comprehension requires tracking three layers at once — the words, the *intentional structure*, and what's salient — and they constrain each other, so a reader running on pattern (the segment layer) while blind to the intentional layer is missing a load-bearing third of the machinery How do readers track segments, purposes, and salience together?.
So the honest synthesis: resonance patterns alone are enough to *detect* — authorship, AI-ness, style, the presence of a device like irony — and they do it astonishingly well. But the corpus is consistent that detection isn't interpretation. Whether that gap matters depends on whether you think meaning is something fixed in the author's head waiting to be recovered, or something that emerges in the reader's encounter with the text. The theory papers assume the former and conclude you need intent; the disagreement and relational-meaning work Can language models learn meaning without engaging the world? quietly suggest the latter, where resonance and reader position do more of the work than we tend to credit. The question doesn't have one answer in this collection — it has two camps, and the more interesting move is noticing which one you already believed.
Sources 10 notes
Bender & Koller argue that meaning requires the relation between expressions and communicative intents. Since LLMs are trained only on form-to-form prediction with no access to shared attention or intent, they cannot reconstruct the meaning that grounds language.
GPT-2 achieves 95% accuracy identifying authorship through style patterns alone, but lacks the evaluative framework to explain why those stylistic choices carry meaning. Detection without interpretation remains cataloguing, not criticism.
General linguistic features combined with argument-quality measures achieved 99% accuracy detecting LLM-generated counter-arguments on r/ChangeMyView, matching heavyweight neural detectors while remaining computationally cheap and transparent. LLMs produce detectable stylistic signatures: accommodation to prompts and textbook-quality argument markers that humans don't replicate.
StoryScope achieved 93.2% accuracy separating AI from human fiction using only discourse-level features like character agency and chronological structure, retaining 97% of performance while eliminating stylistic cues. These structural choices resist humanization because they require rewrites, not surface edits.
LLMs show consistent preference for higher-frequency surface forms over semantically equivalent rare paraphrases across math, machine translation, commonsense reasoning, and tool calling. This suggests models track statistical mass from pretraining rather than meaning-recognition as their primary mechanism.
GPT-4o assigns significantly higher irony scores than humans (p < .001), revealing that LLMs detect irony as a pattern but miscalibrate its prevalence because ironic examples are more salient in training data than in actual use.
AMBIENT benchmark shows GPT-4 correctly disambiguates only 32% of cases versus 90% for humans. This failure spans lexical, structural, and scope ambiguity—revealing that LLMs cannot hold multiple interpretations simultaneously, a fundamental gap hidden by standard benchmarks.
Interpretation Modeling research shows that disagreement on socially embedded sentences reflects valid differences in reader perspective, not annotation failure. Structured human disagreement in NLI benchmarks confirms that interpretation distributions carry meaningful information.
Discourse processing demands parallel recognition of linguistic segments, intentional structure, and attentional salience—not sequential processing. These three layers constrain each other during comprehension, and failures in any single layer disrupt overall understanding.
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.