Mitigating Knowledge Conflicts of Retrieval-Augmented Generation through Dual-Stage Confidence Measurement in Semantic Space

While Retrieval-Augmented Generation (RAG) extends the knowledge boundaries of large language models (LLMs) by incorporating external evidence, it also introduces a critical challenge: the retrieved information may be irrelevant or misleading, and the model's own parametric knowledge may be biased or outdated. As a result, RAG systems face two types of knowledge conflict: 1) Inter-context conflict, which arises when multiple retrieved documents contain mutually inconsistent information; and 2) Context-memory conflict, which occurs when the retrieved context contradicts the model's internal parametric knowledge. Existing methods to solve knowledge conflicts often require additional training, exhibit limited generalization, and typically address these conflict types separately rather than within a unified framework. We propose Dual-Stage Confidence Measurement in Semantic Space (DS-CMS\textsuperscript{2}), a unified training-free framework for mitigating both types of knowledge conflict simultaneously. DS-CMS\textsuperscript{2} measures semantic confidence before and after inference, and uses it to adaptively filter input knowledge sources and adjust the model's output semantic distribution. This dual-stage design enables flexible and principled integration of both internal parametric knowledge and external retrieved knowledge without requiring any additional training. Experiments on multiple benchmarks and LLMs demonstrate that DS-CMS\textsuperscript{2} achieves strong effectiveness and robustness under diverse knowledge conflict settings. Code is provided at https://github.com/hnnuzhangjia/CMS-2.