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Towards Lifelong Model Editing via Simulating Ideal Editor
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Proposes SimIE, which generalizes standard parameter-modifying model editors (ROME, MEMIT, AlphaEdit, etc.) to lifelong editing by recursively adjusting each parameter update with a Moore-Penrose-inspired matrix, approximating an ideal batch editor.
Problem
Standard model editing methods degrade badly when edits are applied sequentially in lifelong settings, while specialized lifelong editors are developed separately; the paper asks whether standard editors can be restored to strong performance in the lifelong regime.
Approach
Define an ideal editor as the minimum-norm solution to a linear system over all edit keys (via Moore-Penrose inverse), then truncate the limiting expression with a regularizer lambda to get a recursive update that rescales each standard-editor update by a matrix P_t^{-1}, requiring only about one line of code.
Key results
- On Llama-2 (7B) ZsRE with 1000 edits, SimIE raises MEMIT's Avg from 0.03 to 0.68.
- AlphaEdit+SimIE reaches Avg 0.80 on Llama-2 ZsRE, outperforming GRACE, PRUNE and the original AlphaEdit.
- On Qwen2.5 (7B), SimIE on AlphaEdit improves over AlphaEdit's Avg by about 4.8% (ROME+SimIE reaches 0.84 vs 0.18 for ROME).
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Apply SimIE to MEND instead of using MEND directly | MEND on Llama-2 (7B) · ZsRE, 1000 edits | Avg: 0.00 -> 0.79 | Helped |
| Apply SimIE to MEMIT instead of using MEMIT directly | MEMIT on Llama-2 (7B) · ZsRE, 1000 edits | Avg: 0.03 -> 0.68 | Helped |
| Apply SimIE to AlphaEdit− instead of using AlphaEdit− directly | AlphaEdit− on Llama-2 (7B) · ZsRE, 1000 edits | Avg: 0.03 -> 0.80 | Helped |
| Apply SimIE to ROME instead of using ROME directly | ROME on Mistral (7B) · Counterfact, 1000 edits | Avg: 0.18 -> 0.66 | Helped |
| Apply SimIE to MEMIT instead of using MEMIT directly | MEMIT on Mistral (7B) · ZsRE, 1000 edits | Avg: 0.03 -> 0.69 | Helped |
| Apply SimIE to AlphaEdit− instead of using AlphaEdit− directly | AlphaEdit− on Qwen2.5 (7B) · ZsRE, 1000 edits | Avg: 0.68 -> 0.87 | Helped |
| Apply SimIE to ROME instead of using ROME directly | ROME on Qwen2.5 (7B) · ZsRE, 1000 edits | Avg: 0.18 -> 0.84 | Helped |
| Use SimIE with AlphaEdit− rather than the specialized AlphaEdit baseline | Llama-2 (7B) · ZsRE, 1000 edits | Avg: 0.76 -> 0.80 | Helped |
| Use SimIE with AlphaEdit− rather than the specialized AlphaEdit baseline | Qwen2.5 (7B) · ZsRE, 1000 edits | Avg: 0.83 -> 0.87 | Helped |
Surprising findings
- Standard editors wrapped with SimIE match or beat dedicated lifelong editors, even though the lifelong baselines were designed specifically for sequential editing.
- Naively running standard editors sequentially collapses to near-zero Avg (e.g., MEMIT 0.03 on Llama-2 ZsRE), showing the failure is largely due to sequential updates rather than the editors themselves.
Limitations
- Relies on two assumptions (over-parameterization and key-value invariance) that may fail in multi-layer editing; theory covers the failure cases only with trade-offs.
- Performance is sensitive to the hyperparameter lambda; very small values cause catastrophic degradation and the best value must be tuned.
- Multi-layer inter-layer dependencies are ignored by applying SimIE independently to each layer.
Experimental setup
Datasets: ZsRE Counterfact
Benchmarks: ZsRE Counterfact
Metrics: Rel Gen Loc Avg Geo
Baselines: FT-L MEND ROME MEMIT AlphaEdit GRACE WISE PRUNE
Builds on: ROME MEMIT AlphaEdit Moore-Penrose inverse
Closest papers by meaning
- MEMOIR: Lifelong Model Editing with Minimal Overwrite and Informed Retention for LLMs (NeurIPS 2025 · similarity 0.91)
- Reinforced Lifelong Editing for Language Models (ICML 2025 · similarity 0.91)
- AlphaEdit: Null-Space Constrained Knowledge Editing for Language Models (ICLR 2025 · similarity 0.91)
- ELDER: Enhancing Lifelong Model Editing with Mixture-of-LoRA (AAAI 2025 · similarity 0.90)
- Rethinking Residual Distribution in Locate-then-Edit Model Editing (NeurIPS 2025 · similarity 0.90)
- Should We Really Edit Language Models? On the Evaluation of Edited Language Models (NeurIPS 2024 · similarity 0.90)
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