Scalable Model Editing via Customized Expert Networks
<|reference_start|>Scalable Model Editing via Customized Expert Networks: Addressing the issues of hallucinations and outdated knowledge in large language models is critical for their reliable application. Model Editing presents a promising avenue for mitigating these challenges in a cost-effective manner. However, existing methods often suffer from unsatisfactory generalization and unintended effects on non-edited samples. To overcome these limitations, we introduce a novel approach: Scalable Model Editing via Customized Expert Networks (SCEN), which is a two-stage continuous training paradigm. Specifically, in the first stage, we train lightweight expert networks individually for each piece of knowledge that needs to be updated. Subsequently, we train a corresponding indexing neuron for each expert to control the activation state of that expert. We conducted a series of experiments on the ZsRE and Hallucination benchmarks by tuning the advanced open-source LLM, Llama2, achieving state-of-the-art results compared to current mainstream methods. Our code is available at https://github.com/TAL-auroraX/SCEN.<|reference_end|>
@article{yao2024scalable,
    title={Scalable Model Editing via Customized Expert Networks},
    author={Zihan Yao, Yu He, Tianyu Qi and Ming Li},
    journal={arXiv preprint arXiv:2404.02699},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.02699},
    primaryClass={cs.CL}
}