Risk Is Not Review Value: Wrong-Answer Exposure Under Bounded Review Budgets
LLM assistants often produce more answers than humans can review before users see them. Most evaluations ask whether an answer is wrong, unsupported, or low-confidence. Bounded review budgets instead ask which answers should be checked first under a fixed review budget. Risk alone is not enough: a high-risk answer may be hard to repair, while a moderately risky answer may be directly correctable from available evidence. For generated-answer evaluation, we model review prioritization as exposure reduction, where review value combines estimated wrongness, intervention affordance, impact, and cost. We evaluate review queues with Wrong-Answer Exposure Ratio (WAER), the fraction of wrong answe...
| Authors | SangJin Park, Myungsub Choi, Jineok Kim et al. |
|---|---|
| Primary category | cs.AI |
| Published | 7 September 2026 |
| Last updated at source | 7 September 2026 |
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