Open-Source Reproduction and Explainability Analysis of Corrective Retrieval Augmented Generation
Corrective Retrieval Augmented Generation (CRAG) improves the robustness of RAG systems by evaluating retrieved document quality and triggering corrective actions. However, the original implementation relies on proprietary components including the Google Search API and closed model weights, limiting reproducibility. In this work, we present a fully open-source reproduction of CRAG, replacing proprietary web search with the Wikipedia API and the original LLaMA-2 generator with Phi-3-mini-4k-instruct. We evaluate on PopQA and ARC-Challenge, demonstrating that our open-source pipeline achieves comparable performance to the original system. Furthermore, we contribute the first explainability ...
| Authors | Surya Vardhan Yalavarthi |
|---|---|
| Primary category | cs.IR |
| Published | 17 March 2026 |
| Last updated at source | 17 March 2026 |
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