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• Neuro-RIT outperforms strong baselines and robustness-enhancing methods across diverse QA benchmarks.result 90%
• Existing approaches to enhance RALM robustness operate via coarse-grained parameter updates at layer or module level.limitation 85%
• Neuro-RIT shifts the paradigm from dense adaptation to precision-driven neuron alignment.method 90%
• The method disentangles neurons responsible for processing relevant versus irrelevant contexts using attribution-based neuron mining.technical 90%
• Neuro-RIT introduces a two-stage instruction tuning strategy that enforces dual capability for noise robustness.method 90%
• The method achieves direct noise suppression by functionally deactivating neurons exclusive to irrelevant contexts.technical 85%
• Retrieval-Augmented Language Models remain vulnerable to performance degradation with irrelevant or noisy retrieved contexts.limitation 85%
• Existing approaches often overlook the inherent neuron-level sparsity of Large Language Models.limitation 80%

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