Scalable Model Editing via Customized Expert Networks
- URL: http://arxiv.org/abs/2404.02699v1
- Date: Wed, 3 Apr 2024 12:57:19 GMT
- Title: Scalable Model Editing via Customized Expert Networks
- Authors: Zihan Yao, Yu He, Tianyu Qi, Ming Li,
- Abstract summary: We introduce scalable Model Editing via Customized Expert Networks (SCEN)
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 neuron for each expert to control the activation state of that expert.
Our experiments on two different sizes of open-source large language models, the Llama2 7B and 13B, achieve state-of-the-art results.
- Score: 10.211286961377942
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Addressing the issue of hallucinations and outdated knowledge in large language models is critical for their reliable application.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 unrelated 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 neuron for each expert to control the activation state of that expert. Our experiments on two different sizes of open-source large language models, the Llama2 7B and 13B, achieve state-of-the-art results compared to existing mainstream Model Editing methods. Our code is available at https: //github.com/TAL-auroraX/SCEN
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