Accurately evaluating the word sense disambiguation (WSD) capabilities of large language models (LLMs) remains challenging, as existing studies primarily rely on single-task evaluations and classification-based metrics that overlook the fundamental differences between generative LLMs and traditional classification models. To bridge this gap, we propose \textbf{RoDEval}, the first comprehensive evaluation framework specifically tailored for assessing LLM-based WSD methods. RoDEval introduces four novel metrics: Disambiguation Scope, Disambiguation Robustness, Disambiguation Reliability, and Definition Generation Quality Score, enabling a multifaceted evaluation of LLMs’ WSD capabilities. Experimental results using RoDEval across five mainstream LLMs uncover significant limitations in their WSD performance. Specifically, incorrect definition selections in multiple-choice WSD tasks stem not from simple neglect or forget of correct options, but rather from incomplete acquisition of the all senses for polysemous words. Instead, disambiguation reliability is often compromised by the models' persistent overconfidence. In addition, inherent biases continue to affect performance, and scaling up model parameters alone fails to meaningfully enhance their ability to generate accurate sense definitions. These findings provide actionable insights for enhancing LLMs' WSD capabilities. The source code and evaluation scripts are open-sourced at https://github.com/DayDream405/RoDEval.