This research investigates the effectiveness of knowledge editing techniques in correcting hallucinations in large language models (LLMs). The authors present HalluEditBench, a comprehensive benchmark that evaluates the performance of different knowledge editing methods across five dimensions: efficacy, generalization, portability, locality, and robustness. They discovered that while some knowledge editing methods show promising results on existing benchmarks, their effectiveness in correcting real-world hallucinations may be significantly lower, highlighting the need for more robust evaluation methods. Additionally, the study provides insights into the limitations of different editing methods, suggesting that no single method excels across all five dimensions. The authors conclude by emphasizing the importance of understanding the potential and limitations of knowledge editing techniques for achieving more accurate and reliable LLMs.