SCoNE: Selective Context-aware Neuron Editing for Robust Retrieval-Augmented Generation
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Computer Science > Computation and Language
Title: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.
| Comments: | Accepted to EMNLP 2026 Main Conference |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2609.00689 [cs.CL] |
| (or arXiv:2609.00689v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2609.00689
arXiv-issued DOI via DataCite (pending registration)
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