# The Butterfly Effect of Model Editing: Few Edits Can Trigger Large Language Models Collapse

arXiv: https://arxiv.org/abs/2402.09656 · HTML: https://mlsanity.com/paper/2402.09656/

## Key facts

- Cited by 54 papers in the map (data through 14 Oct 2026).
- Placed in Model collapse from synthetic data, one of 13 directions in Synthetic data and self-improvement for LLMs.
- First posted on arXiv 15 Feb 2024.

## Placed in

- Synthetic data and self-improvement for LLMs / [Model collapse from synthetic data](https://mlsanity.com/synth/model-collapse-from-synthetic-data/)

## Built on by (most cited first, 30 of 54)

- [AlphaEdit: Null-Space Constrained Knowledge Editing for Language Models](https://mlsanity.com/paper/2410.02355/) (2024)
- [Knowledge Mechanisms in Large Language Models: A Survey and Perspective](https://mlsanity.com/paper/2407.15017/) (2024)
- [Rebuilding ROME : Resolving Model Collapse during Sequential Model Editing](https://mlsanity.com/paper/2403.07175/) (2024)
- [Reinforced Lifelong Editing for Language Models](https://mlsanity.com/paper/2502.05759/) (2025)
- [Coevolving with the Other You: Fine-Tuning LLM with Sequential Cooperative Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2410.06101/) (2024)
- [Perturbation-Restrained Sequential Model Editing](https://mlsanity.com/paper/2405.16821/) (2024)
- [Can Knowledge Editing Really Correct Hallucinations?](https://mlsanity.com/paper/2410.16251/) (2024)
- [The Mirage of Model Editing: Revisiting Evaluation in the Wild](https://mlsanity.com/paper/2502.11177/) (2025)
- [WikiBigEdit: Understanding the Limits of Lifelong Knowledge Editing in LLMs](https://mlsanity.com/paper/2503.05683/) (2025)
- [Avoiding Copyright Infringement via Large Language Model Unlearning](https://mlsanity.com/paper/2406.10952/) (2024)
- [Can Editing LLMs Inject Harm?](https://mlsanity.com/paper/2407.20224/) (2024)
- [Innate Reasoning is Not Enough: In-Context Learning Enhances Reasoning Large Language Models with Less Overthinking](https://mlsanity.com/paper/2503.19602/) (2025)
- [Understanding and Mitigating Gender Bias in LLMs via Interpretable Neuron Editing](https://mlsanity.com/paper/2501.14457/) (2025)
- [Editing the Mind of Giants: An In-Depth Exploration of Pitfalls of Knowledge Editing in Large Language Models](https://mlsanity.com/paper/2406.01436/) (2024)
- [Consecutive Batch Model Editing with HooK Layers](https://mlsanity.com/paper/2403.05330/) (2024)
- [Lifelong Knowledge Editing requires Better Regularization](https://mlsanity.com/paper/2502.01636/) (2025)
- [Related Knowledge Perturbation Matters: Rethinking Multiple Pieces of Knowledge Editing in Same-Subject](https://mlsanity.com/paper/2502.06868/) (2025)
- [CaKE: Circuit-aware Editing Enables Generalizable Knowledge Learners](https://mlsanity.com/paper/2503.16356/) (2025)
- [UltraEdit: Training-, Subject-, and Memory-Free Lifelong Editing in Language Models](https://mlsanity.com/paper/2505.14679/) (2025)
- [Fine-tuning Done Right in Model Editing](https://mlsanity.com/paper/2509.22072/) (2025)
- [ELDER: Enhancing Lifelong Model Editing with Mixture-of-LoRA](https://mlsanity.com/paper/2408.11869/) (2024)
- [Revealing and Mitigating Over-Attention in Knowledge Editing](https://mlsanity.com/paper/2502.14838/) (2025)
- [EvoEdit: Evolving Null-space Alignment for Robust and Efficient Knowledge Editing](https://mlsanity.com/paper/2510.13851/) (2025)
- [MEMLA: Enhancing Multilingual Knowledge Editing with Neuron-Masked Low-Rank Adaptation](https://mlsanity.com/paper/2406.11566/) (2024)
- [Enhancing Data Privacy in Large Language Models through Private Association Editing](https://mlsanity.com/paper/2406.18221/) (2024)
- [COMPKE: Complex Question Answering under Knowledge Editing](https://mlsanity.com/paper/2506.00829/) (2025)
- [Revealing the Deceptiveness of Knowledge Editing: A Mechanistic Analysis of Superficial Editing](https://mlsanity.com/paper/2505.12636/) (2025)
- [SAKE: Towards Editing Auditory Attribute Knowledge of Large Audio-Language Models](https://mlsanity.com/paper/2510.16917/) (2025)
- [Beyond Memorization: A Rigorous Evaluation Framework for Medical Knowledge Editing](https://mlsanity.com/paper/2506.03490/) (2025)
- [Uncovering Context Reliance in Unstructured Knowledge Editing](https://mlsanity.com/paper/2602.19043/) (2026)

## Abstract (arXiv)

Although model editing has shown promise in revising knowledge in Large Language Models (LLMs), its impact on the inherent capabilities of LLMs is often overlooked. In this work, we reveal a critical phenomenon: even a single edit can trigger model collapse, manifesting as significant performance degradation in various benchmark tasks. However, benchmarking LLMs after each edit, while necessary to prevent such collapses, is impractically time-consuming and resource-intensive. To mitigate this, we propose using perplexity as a surrogate metric, validated by extensive experiments demonstrating changes in an edited model's perplexity are strongly correlated with its downstream task performances. We further conduct an in-depth study on sequential editing, a practical setting for real-world scenarios, across various editing methods and LLMs, focusing on hard cases from our previous single edit studies. The results indicate that nearly all examined editing methods result in model collapse after only few edits. To facilitate further research, we have utilized GPT-3.5 to develop a new dataset, HardEdit, based on those hard cases. This dataset aims to establish the foundation for pioneering research in reliable model editing and the mechanisms underlying editing-induced model collapse. We hope this work can draw the community's attention to the potential risks inherent in model editing practices.