Diagnosing and Repairing Factual Errors in RAG under Budget Constraints
Retrieval-Augmented Generation (RAG) improves the factuality of large language models by grounding responses in external evidence, yet real-world deployments remain fragile. Failures often stem from missing or weakly relevant evidence, as well as from generation that does not faithfully reflect the retrieved context. Many existing approaches rely on fine-tuning, privileged access to internal model signals, or resource-insensitive escalation strategies, which limits their practicality in black-box and budget-constrained settings. We propose D2R-RAG (Diagnose-to-Repair RAG), a model-agnostic and resource-aware framework that combines lightweight failure diagnosis with adaptive repair. D2R-R...
| Authors | Soroush Hashemifar, Havva Alizadeh Noughabi, Fattane Zarrinkalam et al. |
|---|---|
| Primary category | cs.AI |
| Published | 28 June 2026 |
| Last updated at source | 28 June 2026 |
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