Feedback Adaptation for Retrieval-Augmented Generation
Retrieval-Augmented Generation (RAG) systems are typically evaluated under static assumptions, despite being frequently corrected through user or expert feedback in deployment. Existing evaluation protocols focus on overall accuracy and fail to capture how systems adapt after feedback is introduced. We introduce feedback adaptation as a problem setting for RAG systems, which asks how effectively and how quickly corrective feedback propagates to future queries. To make this behavior measurable, we propose two evaluation axes: correction lag, which captures the delay between feedback provision and behavioral change, and post-feedback performance, which measures reliability on semantically r...
| Authors | Jihwan Bang, Seunghan Yang, Kyuhong Shim et al. |
|---|---|
| Primary category | cs.CL |
| Published | 8 April 2026 |
| Last updated at source | 16 April 2026 |
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