Knowing Is Not Enough: Information Retrievability as a Precondition to Effective LLM Oversight
Large language models (LLMs) are increasingly embedded in organizational work, yet their errors often pass human review. Prior research locates such failures in users' capability to review LLM output or their engagement in doing so. We develop an alternative, retrieval-based account of human oversight and posit that error detection is more effective when oversight-relevant information is accessible to users at the moment of review. Across two randomized lab-in-the-field experiments with 640 customer-facing employees, we show that self-generated explanations improve error detection and strengthen recall of verification-relevant reasoning, while cues that reactivate such reasoning help sust...
| Authors | Xinyu Fu, Narayan Ramasubbu, Dennis Galletta |
|---|---|
| Primary category | cs.HC |
| Published | 2 September 2026 |
| Last updated at source | 2 September 2026 |
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