The model refuses normal requests: how do you distinguish legitimate limits from false refusals?
For excessive refusals, define permitted business scope and distinguish safety refusal, insufficient evidence and interface failure. Not every absent answer is a false refusal, and improved pass rates must not remove legitimate limits.
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For excessive refusals, define permitted business scope and distinguish safety refusal, insufficient evidence and interface failure. Not every absent answer is a false refusal, and improved pass rates must not remove legitimate limits. Preserve paired normal and should-refuse samples, clarify wording or configuration and evaluate both so normal work succeeds without enabling prohibited actions.
Define answerable and prohibited task boundaries first
This applies to authorized tests mistaken for attacks, ordinary content refused and unexplained inability messages. Preserve the question, context and complete output and inspect official refusal signals. A generic UI error can hide network or permission faults. Check the response before labeling failed authentication an overly restrictive model policy.
Separate refusals, interface errors and insufficient evidence
Inspect system instructions for overly broad prohibitions and requests lacking legitimate purpose or object scope. Record explicit refusal separately from insufficient capability or evidence. Services may expose dedicated refusal stop states; use actual protocols instead of classifying all output from one keyword.
Clarify legitimate tasks while retaining necessary restrictions
Clarify legitimate tasks, required scope and evidence and remove contradictory instructions. Use supported handling that preserves reasons and alternatives instead of bypassing safeguards to force answers. Compare the same test set and distinguish appropriate evidence limits from false refusals. Seeking clarification is not automatically a demand for an unconditional answer.
Count false refusals and inappropriate answers together
Test clearly permitted, clearly prohibited and ambiguous samples separately and check whether fewer false refusals increase inappropriate answers. Preserve labeling rationale and error types and inspect whether only refusal wording changed. Success means more accurate boundaries, not answering everything. Keep necessary clarification, legitimate rejection and incomplete outcomes.
This article called no model, measured no refusal rate and provides no safeguard bypass. Task, permission and service rules determine boundaries in advance. Automatic judges can misclassify cases and need human review. Do not remove difficult samples or relabel genuine risks as harmless merely to improve scores.
Checked sources
These sources support the concepts and methods described here. Examples and test scope are labeled separately; check the version of the tools you use.