# ChainEdit: Propagating Ripple Effects in LLM Knowledge Editing through Logical Rule-Guided Chains

arXiv: https://arxiv.org/abs/2507.08427 · HTML: https://mlsanity.com/paper/2507.08427/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 16 references that are in the map.
- First posted on arXiv 11 Jul 2025.

## Builds on (most cited first, 9 of 9)

- [Locating and Editing Factual Associations in GPT](https://mlsanity.com/paper/2202.05262/) (2022)
- [Mass-Editing Memory in a Transformer](https://mlsanity.com/paper/2210.07229/) (2022)
- [Towards Reasoning in Large Language Models: A Survey](https://mlsanity.com/paper/2212.10403/) (2022)
- [Editing Large Language Models: Problems, Methods, and Opportunities](https://mlsanity.com/paper/2305.13172/) (2023)
- [MQuAKE: Assessing Knowledge Editing in Language Models via Multi-Hop Questions](https://mlsanity.com/paper/2305.14795/) (2023)
- [Evaluating the Ripple Effects of Knowledge Editing in Language Models](https://mlsanity.com/paper/2307.12976/) (2023)
- [Why Does New Knowledge Create Messy Ripple Effects in LLMs?](https://mlsanity.com/paper/2407.12828/) (2024)
- [MEMIT-Merge: Addressing MEMIT's Key-Value Conflicts in Same-Subject Batch Editing for LLMs](https://mlsanity.com/paper/2502.07322/) (2025)
- [RIPPLECOT: Amplifying Ripple Effect of Knowledge Editing in Language Models via Chain-of-Thought In-Context Learning](https://mlsanity.com/paper/2410.03122/) (2024)

## Built on by (most cited first, 4 of 4)

- [Is Model Editing Built on Sand? Revealing Its Illusory Success and Fragile Foundation](https://mlsanity.com/paper/2510.00625/) (2025)
- [Are We Evaluating the Edit Locality of LLM Model Editing Properly?](https://mlsanity.com/paper/2601.17343/) (2026)
- [CLaRE-ty Amid Chaos: Quantifying Representational Entanglement to Predict Ripple Effects in LLM Editing](https://mlsanity.com/paper/2603.19297/) (2026)
- [From Backward Spreading to Forward Replay: Revisiting Target Construction in LLM Parameter Editing](https://mlsanity.com/paper/2605.00358/) (2026)

## Abstract (arXiv)

Current knowledge editing methods for large language models (LLMs) struggle to maintain logical consistency when propagating ripple effects to associated facts. We propose ChainEdit, a framework that synergizes knowledge graph-derived logical rules with LLM logical reasoning capabilities to enable systematic chain updates. By automatically extracting logical patterns from structured knowledge bases and aligning them with LLMs' internal logics, ChainEdit dynamically generates and edits logically connected knowledge clusters. Experiments demonstrate an improvement of more than 30% in logical generalization over baselines while preserving editing reliability and specificity. We further address evaluation biases in existing benchmarks through knowledge-aware protocols that disentangle external dependencies. This work establishes new state-of-the-art performance on ripple effect while ensuring internal logical consistency after knowledge editing.