Beyond Semantic Relevance: Counterfactual Risk Minimization for Robust Retrieval-Augmented Generation
Standard retrieval-augmented generation (RAG) systems rely on semantic relevance and are prone to retrieving confirmatory evidence when users exhibit cognitive biases—such as false premises or confirmation bias—thereby exacerbating hallucinations and creating a “relevance–robustness gap.” This work proposes CoRM-RAG, the first framework to integrate counterfactual risk minimization into RAG. By applying causal interventions, it aligns retrieval with decision safety and introduces a cognitive perturbation protocol to simulate user biases. From this, a lightweight Evidence Critic module is distilled to identify documents with high evidential strength. The approach significantly outperforms existing dense retrievers and rerankers under adversarial queries and enables risk-aware abstention based on robustness scores.