Citation validation in legal and tax AI: why “grounded” matters

A language model produces text that is statistically plausible, not text that is verified. In a legal or tax context that means it will readily generate a section number, a circular reference or a case citation that looks correct and does not exist — or exists but does not say what the surrounding sentence claims.

Why fabrication happens

Citations follow strong surface patterns (“Section 000 of the 0000 Act”, “Circular No. 00/0000”, “X v. Y”). A model that has learned the pattern can fill it with a well-formed but invented value when it lacks the real one, and it does so with the same fluent confidence as a correct answer.

What a validation layer can catch

• Format and existence checks: does this section number exist in this Act; is this a real circular number in the corpus; does this citation resolve to a retrievable document.
• Grounding checks: is every cited authority one that was actually retrieved and passed to the model, rather than one it produced on its own.
• Quote checks: does quoted statutory text match the source verbatim.

What it cannot catch

Whether a real, correctly-cited provision has been applied correctly to the facts; whether a case is still good law; whether the reasoning between the citations is sound. Those remain a professional’s judgement.

How FinRace AI handles it

FinRace AI runs a citation-validation step over generated tax and legal output that checks statute-section and circular references against its corpus and flags authorities the model was not given. It has no case-law database, so it does not validate or generate case citations — see the legal-drafting workspace for what is in scope. Validation reduces fabricated references; it does not replace review by a CA or advocate.