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Efficiently Quantifying and Mitigating Ripple Effects in Model Editing

Jianchen Wang, Zhouhong Gu, Xiaoxuan Zhu, Lin Zhang, Haoning Ye, Zhuozhi Xiong, Hongwei Feng, Yanghua Xiao

TL;DR

This paper proposes a novel evaluation methodology, Graphical Impact Evaluation (GIE), which quantitatively evaluates the adaptations of the model and the subsequent impact of editing, and introduces the Selective Impact Revision (SIR), a model editing method designed to mitigate this ripple effect.

Abstract

Large Language Models have revolutionized numerous tasks with their remarkable efficacy. However, editing these models, crucial for rectifying outdated or erroneous information, often leads to a complex issue known as the ripple effect in the hidden space. While difficult to detect, this effect can significantly impede the efficacy of model editing tasks and deteriorate model performance. This paper addresses this scientific challenge by proposing a novel evaluation methodology, Graphical Impact Evaluation(GIE), which quantitatively evaluates the adaptations of the model and the subsequent impact of editing. Furthermore, we introduce the Selective Impact Revision(SIR), a model editing method designed to mitigate this ripple effect. Our comprehensive evaluations reveal that the ripple effect in the hidden space is a significant issue in all current model editing methods. However, our proposed methods, GIE and SIR, effectively identify and alleviate this issue, contributing to the advancement of LLM editing techniques.

Efficiently Quantifying and Mitigating Ripple Effects in Model Editing

TL;DR

This paper proposes a novel evaluation methodology, Graphical Impact Evaluation (GIE), which quantitatively evaluates the adaptations of the model and the subsequent impact of editing, and introduces the Selective Impact Revision (SIR), a model editing method designed to mitigate this ripple effect.

Abstract

Large Language Models have revolutionized numerous tasks with their remarkable efficacy. However, editing these models, crucial for rectifying outdated or erroneous information, often leads to a complex issue known as the ripple effect in the hidden space. While difficult to detect, this effect can significantly impede the efficacy of model editing tasks and deteriorate model performance. This paper addresses this scientific challenge by proposing a novel evaluation methodology, Graphical Impact Evaluation(GIE), which quantitatively evaluates the adaptations of the model and the subsequent impact of editing. Furthermore, we introduce the Selective Impact Revision(SIR), a model editing method designed to mitigate this ripple effect. Our comprehensive evaluations reveal that the ripple effect in the hidden space is a significant issue in all current model editing methods. However, our proposed methods, GIE and SIR, effectively identify and alleviate this issue, contributing to the advancement of LLM editing techniques.
Paper Structure (27 sections, 12 equations, 6 figures, 7 tables)

This paper contains 27 sections, 12 equations, 6 figures, 7 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Knowledge Editing
  4. Knowledge Editing Evaluation
  5. Preliminary
  6. Our Method
  7. Graphical Impact Evaluation (GIE)
  8. Selective Impact Revision (SIR)
  9. Experiment Setup
  10. Evaluation Dataset Construction
  11. Baseline
  12. Ripple Effect Evaluation Method
  13. Model Editing Method
  14. Metric
  15. Experiment
  16. ...and 12 more sections

Figures (6)

  • Figure 1: The blue bar represents the total change in perplexity across all baselines and evaluation methods. In contrast, the green bar reflects the evaluation cost for all baselines across these methods.
  • Figure 2: The GED's change, with the x-axis representing the iterations of building the Ripple Network of MEMIT. The higher the score is, the more structural difference the two graphs have.
  • Figure 3: the frequency of node degrees within the vanilla KG, GIE network, and Ripple Network of MEMIT.
  • Figure 4: The frequency distribution of perplexity changes after model editing.
  • Figure 5: Average changing in perplexity attributed to SIR. The left panel shows the overall perplexity change, while the right panel shows the perplexity change for the triplets most similar to the edit targets.
  • ...and 1 more figures