arXiv:2606.05644v1 Announce Type: new Abstract: When retrieved evidence contradicts parametric memory, language models frequently ignore context and default to memorized priors -- a failure that undermines the core purpose of retrieval augmentation. Contrastive decoding amplifies the context-conditioned output to suppress parametric bias, but existing methods rest on an implicit assumption that this bias is uniform across tokens. A single global contrastive weight over-penalizes safe tokens while leaving genuinely conflicted ones insufficiently corrected. We identify token-level conflict conce
The proliferation of Retrieval-Augmented Generation (RAG) systems highlights a core challenge in aligning LLM outputs with external data, making solutions for conflict resolution highly relevant.
This research addresses a fundamental limitation in RAG systems where LLMs prioritize memorized priors over retrieved evidence, potentially leading to inaccurate or unfaithful outputs.
Improving RAG fidelity by better handling retrieval-memory conflicts means more reliable AI agents that can accurately ground responses in provided context rather than hallucinating or defaulting to outdated knowledge.
- · AI developers
- · Enterprise AI adoption
- · Users of RAG-based AI
- · Ineffective RAG implementations
- · Applications requiring high factual accuracy without advanced conflict resolutio
More accurate and trustworthy AI models, particularly in data-intensive applications.
Accelerated deployment of AI agents in critical domains where factual accuracy is paramount.
Enhanced confidence in AI systems could broaden their application across currently sensitive or high-stakes sectors.
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Read at arXiv cs.AI