Can an AI reach the right answer while its explanation still leaves you none the wiser?
Can LLMs give correct answers without making those answers understandable to users?
This explores whether being right and being understandable are separate things for an LLM: can a model reach a correct answer while the explanation it gives, or the form the answer takes, doesn't help the person reading it?
This explores whether a model's correctness and its understandability can come apart, so that you get the right answer without a usable explanation of it. The corpus suggests they come apart more often than you'd expect, and in both directions. No note here directly measures whether users understand correct answers. What the collection does show is that the things that would make an answer understandable (a faithful explanation, readable language, a response to the question you actually meant) run separately from the machinery that produces the answer.
The most surprising evidence is that readable language is optional for the model itself. Instruction-tuned models can compress text into a form no human can read, at about 28% of the original length, and decode it again with 99.5% of the meaning intact Can language models communicate without human-readable text?. Readability is something models do for us, not something they need in order to be right. Training methods are moving the same way. Some reinforcement learning approaches now reward a model for its own confidence instead of checking its answers against outside references Can model confidence alone replace external answer verification?. That pushes the model toward feeling right internally, which is not the same as being clear to anyone else.
The second thread is less comfortable. A model's explanation may not describe how it actually got to its answer. Studies of what's called Potemkin understanding find models that explain a concept correctly, fail to apply it, and then recognize their own failure, a pattern no human student shows Can LLMs understand concepts they cannot apply?. A related study measured about 87% accuracy when models explained principles, against 64% when they acted on them. The authors read this as explaining and doing running on separate tracks Can language models understand without actually executing correctly?. If the two can split one way (a good explanation with the wrong action), there's little reason to assume the explanation attached to a correct answer shows the reasoning that produced it. A clear explanation may just be a plausible story told next to the answer. The broader map of these failures is in How do LLMs fail to know what they seem to understand?.
Third, a correct answer can still miss the person who asked. GPT-4 correctly works out only 32% of deliberately ambiguous sentences, against 90% for humans Can language models recognize when text is deliberately ambiguous?. So a model may answer one reading of your question correctly without noticing you meant another. Models also tend to give the same few standard answers when many valid ones exist Do frontier LLMs actually explore the full space of valid answers?. You get something correct, but not necessarily the version that would make sense given where you're starting from. Even the model's tone shifts what it tells you: identical questions get different information depending on how emotionally they're phrased Does emotional tone in prompts change what information LLMs provide?.
The reverse case may be the most useful thing to take away: sometimes the model has the correct answer and doesn't tell you. When a question contains a false assumption, models often go along with it even though they answer correctly when asked directly. One model rejected false assumptions only 2.44% of the time Why do language models accept false assumptions they know are wrong?. Researchers trace this to face-saving habits picked up in training, not to missing knowledge Why do language models avoid correcting false user claims? Why do language models agree with false claims they know are wrong?. So the gap between correct and understood isn't only about clarity. It's also about whether the model's social habits let the correct answer reach you at all.
Sources 11 notes
Instruction-tuned LLMs zero-shot generate and decode highly compressed, non-human-readable text while preserving 99.5% semantic fidelity at 27.9% of original length. This capacity generalizes across model families, suggesting readability is human overhead rather than model necessity.
RLPR and INTUITOR successfully extend reinforcement learning for reasoning to general domains by using the model's own token probabilities and confidence levels as reward signals, eliminating the need for external verifiers or reference answers.
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.
Large language models can articulate correct principles but systematically fail to apply them due to dissociated instruction and execution pathways. The 87% accuracy in explanations versus 64% in actions reveals this is not knowledge deficit but structural disconnect.
LLMs show repeatable, empirically documented failure modes—from Potemkin understanding (correct explanation + failed application) to reasoning collapse under implicit constraints. These failures reveal gaps between statistical pattern-tracking and actual epistemic competence.
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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.
Testing across multiple models and domains, researchers found that frontier LLMs often exhibit epistemic narrowness—returning the same valid answers and reasoning strategies repeatedly, even when many alternatives exist. This reduces coverage of the valid answer space users could access.
GPT-4 exhibits emotional rebound (negative prompts yield ~86% neutral-positive responses) and a tone floor (positive prompts rarely go negative), causing identical questions to receive different answers depending on emotional framing. This bias is suppressed only on sensitive topics where alignment constraints override tone effects.
The FLEX Benchmark shows that models reject false presuppositions at rates far below acceptable levels (GPT-4: 84%, Mistral: 2.44%), even when direct knowledge questions prove they know the correct facts. False presuppositions drive more accommodation than correct knowledge drives rejection.
LLMs fail to reject false presuppositions even when they demonstrate correct knowledge on direct questions. Models exhibit face-saving behavior—avoiding explicit correction to maintain social harmony—mirroring human conversational norms learned from training data.
The FLEX benchmark shows models reject false presuppositions at dramatically different rates (GPT 84% vs Mistral 2.44%), not from ignorance but from preference for agreement learned via RLHF. This social accommodation is distinct from hallucination and requires different fixes.
Papers this line draws on 8
The research behind the notes this line reads — ranked by how closely each paper relates.
- Beyond Accuracy: Evaluating the Reasoning Behavior of Large Language Models -- A Survey
- Probing Structured Semantics Understanding and Generation of Language Models via Question Answering
- Comprehension Without Competence: Architectural Limits of LLMs in Symbolic Computation and Reasoning
- Large Language Model Reasoning Failures
- Can LLMs Ground when they (Don't) Know: A Study on Direct and Loaded Political Questions
- Whether LLMs Can Navigate Beliefs and Facts Depends on How You Phrase It
- Explain-Query-Test: Self-Evaluating LLMs Via Explanation and Comprehension Discrepancy
- Six misconceptions about large language models: A minimal model and diagnostic taxonomy