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UniEdit: A Unified Knowledge Editing Benchmark for Large Language Models

arXiv — cs.CL•Wednesday, November 12, 2025 at 5:00:00 AM
NeutralArtificial Intelligence
The introduction of UniEdit marks a significant step forward in the field of large language models (LLMs), as it provides a unified benchmark designed to overcome the limitations of current editing datasets, which are often restricted to narrow knowledge domains. By leveraging a Neighborhood Multi-hop Chain Sampling (NMCS) algorithm, UniEdit samples subgraphs from 25 common domains, ensuring a broad and comprehensive evaluation of editing demands. This approach not only enhances the accuracy and reliability of LLMs but also addresses the diverse ripple effects that can arise from edits. Proprietary LLMs are employed to convert these sampled knowledge subgraphs into natural language text, ensuring grammatical accuracy and syntactical diversity. Extensive statistical analysis confirms the scale and comprehensiveness of the UniEdit benchmark, while comprehensive experiments across multiple LLMs and editors provide insights into their performance. This initiative is essential for advancing…
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