Attribution and Citation: Sourcing LLM Outputs › Attribution Accuracy

Producing citations is necessary but not sufficient. A model can produce citations that are wrong in several distinct ways, and each failure mode has different consequences for the user.

Unsupported citations: The model cites a passage that does not support the claim. For example, the model says "The heart has four chambers [1]" but passage [1] discusses blood pressure, not cardiac anatomy. The citation is present but irrelevant. This is perhaps the most dangerous failure mode because it looks like correct attribution while providing no verification value.

Overclaiming: The model makes a claim that goes beyond what the source says. A source might say "in some experimental conditions, treatment X reduced inflammation by 20% in mice," and the model summarizes this as "treatment X reduces inflammation," citing the same source. The direction of the claim is correct, but it omits essential caveats: the experimental conditions, the magnitude, and the species. The citation technically points to a related source, but the claim is stronger than what the evidence supports.

Missing citations: The model makes factual claims without any citation. This is not strictly wrong if the claims are common knowledge, but for specific or technical assertions, missing citations leave the reader unable to verify. A model that claims "the GDP of Germany in 2023 was $4.1 trillion" without citing a source has made a specific empirical claim that requires evidence, even if the claim happens to be accurate.

Citation to wrong source: In a multi-document RAG setting, the model supports a claim by citing document 3, but the supporting information is in document 1. The claim may be correct, and the information may even be consistent with document 3, but the specific citation is wrong. This matters when the user wants to follow up: they go to document 3 and cannot find the passage they were supposed to verify.

Copied but unattributed: The model reproduces a specific phrase or finding verbatim from a source without citing it. This is a form of attribution failure that may also constitute a copyright concern. Models trained on large text corpora sometimes reproduce training data near-verbatim, and this reproduction may or may not be accompanied by proper attribution.

Each of these failure modes requires different detection strategies. Unsupported citations and overclaiming can be detected by NLI-based entailment checking. Missing citations require identifying claims and comparing them against a citation registry. Citations to wrong sources require checking that the cited passage is the one from which the relevant information was drawn, not just that it is topically related. Copied but unattributed text can be detected using plagiarism detection approaches.