bibtext: @inproceedings{
  yao2024scalable,
  title={Scalable Model Editing via Customized Expert Networks},
  author={Zihan Yao and Yu He and Tianyu Qi and Ming Li},
  booktitle={First Conference on Language Modeling},
  year={2024},
  url={https://openreview.net/forum?id=dJfBejh478}
  }
abstract: Addressing the issues of hallucinations and outdated knowledge in large language models is critical for their reliable application.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 costeffective 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-theart results compared to current mainstream methods. Our code is available at https://github.com/TAL-auroraX/SCEN.
title: Scalable Model Editing via Customized Expert Networks
authors: Zihan Yao Yu He Tianyu Qi Ming Li