Summary of Reasons and Solutions For the Decline in Model Performance After Editing, by Xiusheng Huang et al.
Reasons and Solutions for the Decline in Model Performance after Editing
by Xiusheng Huang, Jiaxiang Liu, Yequan Wang, Kang Liu
First submitted to arxiv on: 31 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper investigates the performance degradation of knowledge editing technology in large-scale language models. Recent research has shown that edited models often exhibit varying degrees of performance decline, but the reasons behind this phenomenon and potential solutions have not been provided. To address this issue, the authors explore the underlying reasons from both data and model perspectives. From a data perspective, they construct a Multi-Question Dataset (MQD) to evaluate the impact of different types of editing data on model performance. The results indicate that the diversity of editing targets and sequence length are key factors affecting model performance. From a model perspective, the authors identify a strong correlation between the L1-norm of the editing model layer and editing accuracy, highlighting an important factor leading to the bottleneck of editing performance. To overcome this limitation, the paper proposes a Dump for Sequence (D4S) method, which successfully reduces the L1-norm of the editing layer, allowing users to perform multiple effective edits while minimizing model damage. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores why edited language models often don’t work as well as they used to. Researchers found that when they fixed mistakes or updated knowledge in large language models, the models didn’t always perform better. To figure out why this happens and how to make it better, the authors looked at both the data being used and the model itself. They created a special dataset to test different types of editing data on model performance. The results show that if the edited targets are diverse and the sequence length is longer, the model performs better. From looking at the model itself, they found that how accurate the editing process is affects its overall performance. To fix this issue, the authors created a new method called Dump for Sequence (D4S) that helps reduce damage to the model while allowing users to make many edits. |
