Large Language Models Understand Language

Claim: Large language models genuinely understand and comprehend human language rather than merely predicting tokens through statistical pattern matching.

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Model answers are probabilities, not verdicts.

Dimension 01 / 10

Error feedback

34.4%Bullshit weight
Model confidence30.4%

How does the examined system (claim, method or field) respond to inputs that directly contradict its predictions or assumptions?

  • 0 · 0.00It adjusts its parameters, narrows its scope or rejects the refuted hypothesis on the basis of repeated anomalies.59.41%
  • 1 · 0.85It produces unverifiable ad-hoc explanations whose only function is to protect the core claim from refutation.29.77%
  • 2 · 1.00It interprets every contrary piece of evidence as proof of a deliberate conspiracy, data manipulation or a cover-up by outside institutions.1.54%
  • 3 · 0.95It blames the missing result on the recipient's wrong intention, lack of faith or insufficient commitment.1.04%
  • 4 · 0.80It avoids confronting error by making predictions so vague that any outcome can be called a success after the fact.8.25%

The bar shows the probability the model gave each answer. The weight shows how much that answer counts as bullshit.

10 decision dimensions

View JSON
DimensionBullshit weightModel confidence
Error feedback34.4%30.4%
Causal carrier7.7%73.8%
Blinding test38.6%7.4%
Falsifiability73.5%36.2%
Semantic precision65.4%31.6%
Ockham's razor21.0%54.0%
Measurement network44.7%20.3%
Operator independence26.3%33.7%
Model evolution44.4%17.6%
Risk asymmetry23.3%46.6%
Model provenanceliquid/d1-ultra-20261015 · Oct 30, 2026

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