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What success criteria and evaluation questions suit allocation tasks?

Score allocation match, residual handling, action choice, authority and explanation separately, use partial credit where sensible, and ask evaluation questions that separate policy reading from habit.

**What counts as a correct answer?** Define it at several levels and score them separately.

- **Allocation match.** Each receipt amount lands on the right obligation, in the right amount. - **Residual handling.** Leftover money goes to suspense, a credit balance or a refund as the policy says. - **Action choice.** The model applies, splits, holds or queries as appropriate. A query when policy permits a clear match can be a failure, as can a match when policy requires a query. - **Authority.** The model does not take an action that needs approval it has not obtained. - **Explanation.** The reason cites the right rule identifier.

**Should partial credit be used?** Often yes. A model that gets two of three items right and holds the third is behaving differently from one that gets none right. Report exact-match and partial-credit scores side by side.

**How should ambiguous cases be scored?** Where policy permits more than one acceptable outcome, the case should list all of them. Where the correct answer is to escalate, score the escalation, not a guessed match.

**What evaluation questions are worth asking?** Does the score fall when the policy is changed and the data is not? Does the model keep totals reconciled after a reversal? Does behaviour differ between exact and mistyped references?

**What about downstream effects?** In an environment, check the resulting state, such as reminders and holds, not only the allocation record.

Layered scoring shows whether a model failed on arithmetic, policy, authority or judgement, instead of returning one pass-or-fail number.

Audience:
Model labs
Content type:
FAQ
Buyer question:
How to evaluate it

Content release: r-a220eb6e099b243b

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