Can LLM semantic representations exist without causally influencing their generation output?
This explores whether an LLM can hold an internal 'meaning' in its activations that doesn't actually drive what it writes — and the corpus turns out to be less about secret dormant representations than about how hard it is to prove any representation causes the output at all.
This explores whether an LLM can hold a semantic representation internally that doesn't causally shape its generation — and the most direct answer in the corpus is methodological: you cannot even claim a representation exists in a meaningful way until you show it causally moves the output. The cleanest statement of this is that mechanistic understanding requires *both* representational and causal analysis Can we understand LLM mechanisms with only representational analysis?. Finding a feature that *correlates* with a concept tells you nothing about whether the model uses it; only intervening — perturbing the representation and watching the generation change — establishes that the thing you found is load-bearing rather than a bystander. So a 'semantic representation with no causal influence' is, by this framing, exactly what representational analysis alone keeps accidentally discovering: correlations masquerading as mechanisms.
The flip side is that representations demonstrably *can* be made to drive output. LatentQA trains a decoder to read an LLM's activations into plain language and then steer behavior by gradient descent on those same activations Can we decode what LLM activations really represent in language?. That's the existence proof in the other direction — when a representation is real and connected, you can both read it and push on it to change what the model says. The interesting tension between these two notes is that 'decodable' and 'causal' are not the same property: you can sometimes decode something that doesn't drive behavior, which is precisely why the mechanistic note insists on pairing the two.
Where the corpus gets surprising is the Potemkin-understanding work, which looks like a case of representation-without-causal-influence in the wild Can LLMs understand concepts they cannot apply?. A model explains a concept correctly, fails to apply it, and then recognizes its own failure — a pattern the authors read as functionally *disconnected* explanation and execution pathways. The 'correct explanation' representation is present but doesn't govern the 'apply it' generation. That's close to a behavioral signature of a semantic representation that exists but doesn't causally reach the output path it should. The decoupling-semantics result rhymes with this: strip the familiar semantic content out of a reasoning task and performance collapses even when the correct rules are sitting in context Do large language models reason symbolically or semantically? — the rules are represented but don't drive the computation the way a symbolic reasoner's would.
There's a deeper, almost deflationary thread worth pulling: maybe what looks like 'semantic representation' is often just statistical mass. Models systematically prefer higher-frequency surface phrasings over semantically identical rare ones Do language models really understand meaning or just surface frequency?, which suggests the thing causally steering generation isn't meaning at all but token frequency — the 'semantics' you'd hope to find may be downstream decoration rather than the driver. Pair that with the view that LLMs realize meaning purely through relational compression of text, with no external referent Can language models learn meaning without engaging the world?, and the question reframes itself: it's not that representations float free of generation, it's that 'semantic representation' and 'generation statistics' may be far more entangled than the clean separation the question imagines.
If you want the doorway that makes this concrete, start with the mechanistic note for *why the question is hard to even pose rigorously*, LatentQA for *what causal access actually looks like*, and Potemkin for *the closest thing to a representation that fails to reach the output*. Together they suggest the honest answer is: representations that genuinely don't influence generation are, by current methods, indistinguishable from representations that were never functional — and the disconnects we *can* observe (explain-but-can't-apply) live in the wiring between pathways, not in some inert semantic store.
Sources 6 notes
Representational analysis alone identifies correlations without causation; causal analysis alone shows behavioral effects without explaining them. Only paired methods—locating candidate features representationally, then verifying causally—produce complete mechanistic claims.
LatentQA trains a decoder to answer natural language questions about LLM activations, enabling both interpretability (understanding what activations encode) and controllability (steering them via gradient descent). Critical design choices—activation masking, diverse training data, and faithful completions—proved essential for generalization.
Models can explain concepts accurately, fail to apply them, and recognize the failure—a triple pattern incompatible with human cognition. This indicates functionally disconnected explanation and execution pathways rather than simple knowledge gaps.
When semantic content is decoupled from reasoning tasks, LLM performance collapses even with correct rules in context. Models rely on parametric commonsense and token associations rather than formal logical manipulation, constraining reasoning to training distribution semantics.
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.
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.