Although retrieval-augmented generation (RAG) technology mitigates the hallucination issue in large language models (LLMs) by incorporating external knowledge, and combining reasoning models can further enhance RAG system performance, retrieval noise and attention bias still lead to the diffusion of factual errors in problems such as factual queries, multi-hop questions, and unanswerable questions. Existing methods struggle to effectively suppress “high-confidence hallucinations” in long-chain reasoning due to their failure to decouple knowledge bias effects from causal reasoning paths. To address this, this paper proposes the ISFJ-RAG framework, which dynamically intervenes in hallucinations through counterfactual joint decoding. First, a structural causal model (SCM) reveals three root causes of hallucinations in RAG systems: irrelevant knowledge interference, reasoning path bias, and spurious correlations in self-attention mechanisms. A dual-decoder architecture is further designed: the total causal effect decoder models the global relationship between user queries and knowledge, while the knowledge bias effect decoder captures potential biases induced by external knowledge. A dynamic modulation module converts the latter’s output into a proxy measure of hallucination bias. By computing individual treatment effects (ITEs), the bias component is removed from the full generation distribution, achieving simultaneous suppression of knowledge-irrelevant and reasoning-irrelevant hallucinations. Ablation experiments validate the robustness of average token log-probability as a confidence metric. Experiments demonstrate that on the RAGEval benchmark, ISFJ-RAG improves generation completeness to 86.89% (+5.49%) while reducing hallucination rates to 10.39% (−2.5%) and irrelevance rates to 4.44% (−2.99%).
Liu et al. (2026) studied this question.