Mitigating Context-Memory Conflicts in LLMs through Dynamic Cognitive Reconciliation Decoding

arXiv:2605.12185v1 Announce Type: cross Abstract: Large language models accumulate extensive parametric knowledge through pre-training. However, knowledge conflicts occur when outdated or incorrect parametric knowledge conflicts with external knowledge in the context. Existing methods address knowledge conflicts through contrastive decoding, but in conflict-free scenarios, static approaches disrupt output distribution. Other dynamic decoding methods attempt to measure the degree of conflict but