SCoNE: Selective Context-aware Neuron Editing for Robust Retrieval-Augmented Generation

Abstract:Retrieval-Augmented Generation (RAG) is highly sensitive to retrieval noise: when retrieved documents mix informative and irrelevant context, LLMs are easily distracted, leading to hallucinations. To overcome this, we propose SCoNE (Selective Context-aware Neuron Editing), a training-free model editing approach that improves retrieval noise robustness by selectively strengthening context-aware FFN neurons that are identified by both high attribution and high cross-input variability. SCoNE requires only a small number of mining samples, no fine-tuning, and no inference-time overhead. Across various knowledge-intensive question-answering benchmarks and two LLM backbones, SCoNE consistently outperforms competitive baseline methods. Our code is available at this https URL.

Computation and Language (cs.CL)

arXiv:2609.00689 [cs.CL]

(or arXiv:2609.00689v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.00689 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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