# UniEdit: A Unified Knowledge Editing Benchmark for Large Language Models

arXiv: https://arxiv.org/abs/2505.12345 · HTML: https://mlsanity.com/paper/2505.12345/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 May 2025.

## Built on by (most cited first, 10 of 10)

- [ThinkEval: Practical Evaluation of Knowledge Leakage in LLM Editing using Thought-based Knowledge Graphs](https://mlsanity.com/paper/2506.01386/) (2025)
- [Are We Evaluating the Edit Locality of LLM Model Editing Properly?](https://mlsanity.com/paper/2601.17343/) (2026)
- [From Backward Spreading to Forward Replay: Revisiting Target Construction in LLM Parameter Editing](https://mlsanity.com/paper/2605.00358/) (2026)
- [Scaling Laws for Hypernetwork-Based Knowledge Injection in Large Language Models](https://mlsanity.com/paper/2607.19604/) (2026)
- [AutoGuard: A Self-Healing Proactive Security Layer for DevSecOps Pipelines Using Reinforcement Learning](https://mlsanity.com/paper/2512.04368/) (2025)
- [Reversible Lifelong Model Editing via Semantic Routing-Based LoRA](https://mlsanity.com/paper/2603.11239/) (2026)
- [EditPropBench: Measuring Factual Edit Propagation in Scientific Manuscripts](https://mlsanity.com/paper/2605.02083/) (2026)
- [Benchmarking Safety Risks of Knowledge-Intensive Reasoning under Malicious Knowledge Editing](https://mlsanity.com/paper/2605.10146/) (2026)
- [An Information-Theoretic Framework for Robust Large Language Model Editing](https://mlsanity.com/paper/2512.16227/) (2025)
- [MedKIT: Evaluating Knowledge Integration and Generalization in Large Language Models](https://mlsanity.com/paper/2609.38543/) (2026)

## Abstract (arXiv)

Model editing aims to enhance the accuracy and reliability of large language models (LLMs) by efficiently adjusting their internal parameters. Currently, most LLM editing datasets are confined to narrow knowledge domains and cover a limited range of editing evaluation. They often overlook the broad scope of editing demands and the diversity of ripple effects resulting from edits. In this context, we introduce UniEdit, a unified benchmark for LLM editing grounded in open-domain knowledge. First, we construct editing samples by selecting entities from 25 common domains across five major categories, utilizing the extensive triple knowledge available in open-domain knowledge graphs to ensure comprehensive coverage of the knowledge domains. To address the issues of generality and locality in editing, we design an Neighborhood Multi-hop Chain Sampling (NMCS) algorithm to sample subgraphs based on a given knowledge piece to entail comprehensive ripple effects to evaluate. Finally, we employ proprietary LLMs to convert the sampled knowledge subgraphs into natural language text, guaranteeing grammatical accuracy and syntactical diversity. Extensive statistical analysis confirms the scale, comprehensiveness, and diversity of our UniEdit benchmark. We conduct comprehensive experiments across multiple LLMs and editors, analyzing their performance to highlight strengths and weaknesses in editing across open knowledge domains and various evaluation criteria, thereby offering valuable insights for future research endeavors.