FIDES detects token-level retrieval-memory conflicts via output, hidden, and trajectory signals to selectively apply contrastive decoding, raising context fidelity by 3-13 points over baselines across 18 settings on models up to 70B.
InProceedings of the 2021 Conference on Empirical Methods in Natural Language Process- ing, pages 7052–7063, Online and Punta Cana, Do- minican Republic
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Pretrained lexical priors in language models persist despite explicit remapping rules, as shown by a Stroop paradigm where prior strength predicts interference and activation patching localizes the repair mechanism.
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FIDES: Faithful Inference via Deep Evidence Signals for Retrieval-Memory Conflict in RAG
FIDES detects token-level retrieval-memory conflicts via output, hidden, and trajectory signals to selectively apply contrastive decoding, raising context fidelity by 3-13 points over baselines across 18 settings on models up to 70B.