DiSCo: Balancing Parametric and Contextual Knowledge by Decoupling the Direction and Strength of Contextual Influence

Retrieval-augmented generation (RAG) integrates two knowledge sources: parametric knowledge encoded in language models and contextual knowledge provided by retrieved evidence. Neither source is consistently more reliable than the other: parametric knowledge may be incomplete or outdated, while retrieved evidence may be incorrect or noisy. Consequently, decoding methods that consistently favor one source can perform poorly when the other is more reliable. To address this challenge, we propose DiSCo, an adaptive decoding framework that regulates contextual influence by decoupling its direction and strength at each decoding step. DiSCo computes next-token distributions with and without context, derives relative confidence from the difference in their top-1 log probabilities, and measures distributional disagreement using Jensen–Shannon divergence. Relative confidence determines whether the retrieval-induced logit shift should be suppressed or amplified, while distributional disagreement controls the adjustment strength. To implement this decoupled control, DiSCo maps the two signals to soft linguistic states and aggregates them through a nine-rule Takagi–Sugeno fuzzy model, producing a token-wise coefficient that scales the retrieval-induced logit shift relative to the parametric logits. Experiments on four QA datasets and one knowledge-conflict dataset show that DiSCo outperforms both standard and state-of-the-art decoding methods on average while remaining robust under varying context reliability and conflict proportions.