Word Sense Disambiguation (WSD) is a historical task in computational linguistics that has received much attention over the years. However, with the advent of Large Language Models (LLMs), interest in this task (in its classical definition) has decreased. In this study, we evaluate the performance of various LLMs on the WSD task. We extend a previous benchmark (XL-WSD) to re-design two subtasks suitable for LLM: 1) given a word in a sentence, the LLM must generate the correct definition; 2) given a word in a sentence and a set of predefined meanings, the LLM must select the correct one. The extended benchmark is built using the XL-WSD and BabelNet. The results indicate that LLMs perform well in zero-shot learning but cannot surpass current state-of-the-art methods. However, a fine-tuned model with a medium number of parameters outperforms all other models, including the state-of-the-art.

本研究探讨了大型语言模型在词义消歧（WSD）任务中的表现，填补了这一经典任务逐渐被忽视的空白。通过扩展XL-WSD基准，设计了适合于大型语言模型的两个子任务，结果显示虽然大型语言模型在零-shot 学习上表现良好，但只有经过微调的模型才能超过其他模型的表现。这项工作的影响在于，提供了新的视角来理解大型语言模型在语义理解中的潜力。

探索大型语言模型的词义消歧能力