Papers by Zilu Dong
MEMIT-Merge: Addressing MEMIT’s Key-Value Conflicts in Same-Subject Batch Editing for LLMs (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing knowledge editing techniques that modify models’ internal knowledge without full model retraining have gained significant attention. |
| Approach: | They propose an enhanced approach that merges value computation processes for facts sharing the same subject to improve editing efficiency. |
| Outcome: | The proposed method maintains a 98% editing success rate on same-subject and distinct-sub subject datasets while the original success rate drops to 46%. |
ChainEdit: Propagating Ripple Effects in LLM Knowledge Editing through Logical Rule-Guided Chains (2025.acl-long)
Copied to clipboard
| Challenge: | Existing knowledge editing methods for large language models struggle to maintain logical consistency when propagating ripple effects to associated facts. |
| Approach: | They propose a framework that synergizes knowledge graph-derived logical rules with LLM logical reasoning capabilities to enable systematic chain updates. |
| Outcome: | The proposed framework improves logical generalization and specificity while maintaining reliability and specificness. |