A creator digital twin can answer from approved biography, public interviews, current product documents and general model knowledge. Those sources do not carry equal authority. A trustworthy twin should know when it is speaking from creator-approved information, when it is summarizing a public source and when it is making a general inference. Source-attribution benchmarks test that separation.

Create source-labeled questions

Build questions whose correct answer lives in one known source class. Keep creator-approved, public and absent-information cases separate.

Test approved facts. Biography and membership rules should use the canonical creator source. The twin should not prefer a random third-party page.

Test historical public statements

Old interviews can answer “what did the creator say then?” They should not silently become current policy.

Test absent knowledge. Ask about private plans and unpublished partnerships. The correct response is often uncertainty, not invention.

Measure citation accuracy

If the interface shows sources, the cited passage must support the claim. Related but non-supporting links should count as failures.

Measure authority selection. When two sources conflict, the benchmark should know which one should win. Authority and freshness can be scored separately.

Test live corrections

A creator correction should outrank older public material. Repeat the question after reindexing to check persistence.

Include multilingual retrieval. The same approved fact may exist in several languages. Translation should not change source authority.

Separate inference language

The twin may synthesize a reasonable conclusion from several sources. It should phrase that as interpretation rather than creator-approved fact.

Test retrieval without citation UI. Even if users do not see sources, internal attribution should remain traceable. Operators need evidence during support and disputes.

Measure stale-source errors

Old prices and events provide a clear freshness benchmark. Track whether the twin retrieves superseded documents.

Use confidence alongside source class. A high-authority source can still be ambiguous. The system should express uncertainty where the source itself is uncertain.

Evaluate after knowledge updates

Source ranking can change after new documents enter the index. Run the fixed attribution set after every major update.

Knowledge freshness protocols explain how sources should evolve. Attribution benchmarks test whether the deployed twin actually follows those rules when it answers fans. Test answers against known source authority. Create questions whose correct answers live in different places: an approved creator biography, a current product sheet, an old interview, or nowhere in the authorized knowledge base. Give the evaluator the expected source class and ask whether the digital twin attributed the statement appropriately.

A particularly important scenario is a changed commercial fact. If an old interview quotes one price and a current approved sheet gives another, the system should use the current authoritative source and avoid confidently repeating obsolete numbers.

Measure source support at the claim level

Checking that an answer contains a citation is insufficient. A citation must support the particular claim it follows, and the model should distinguish sourced facts from its own general explanation. Unsupported private statements should be refused or clearly marked unknown. Retest after a knowledge-base update, including in multiple languages. Correct retrieval in English does not guarantee that translated questions will respect the same source hierarchy. Store the source version alongside each evaluation result to make regressions reproducible.

Include examples where a correct-looking answer is attached to the wrong source. If two documents happen to contain similar language, the model should cite the source that actually establishes the claim instead of whichever passage was retrieved first. Citation precision and source authority need separate scoring because a plausible citation can disguise a provenance error.

Creator-specific questions also require a boundary between public facts and private intent. The model can summarize an approved statement from a published interview but should not turn that into a promise about a future partnership, endorsement or appearance. A system that uses general world knowledge to fill that gap may appear fluent while misrepresenting the human creator.

Each benchmark run should record the approved source versions. Otherwise, a new fact arriving during an evaluation can make an earlier answer look wrong even though it was correct for the data available at that time. Versioning is essential for reproducible tests and fair comparisons between model updates.

The evaluation should also verify the source of a negative claim. For example, an old interview may mention a discontinued partnership, while the current approved knowledge base says nothing about a renewal. A digital twin should not transform that historical fact into an active commercial endorsement. Source attribution must preserve time and scope, not simply attach the name of a document.