We evaluate a battery of recent large language models on two benchmarks for word sense disambiguation in Swedish. At present, all current models are less accurate than the best supervised disambiguators in cases where a training set is available, but most models outperform graph-based unsupervised systems. Different prompting approaches are compared, with a focus on how to express the set of possible senses in a given context. The best accuracies are achieved when human-written definitions of the senses are included in the prompts.

翻译：我们评估了一系列近期发布的大型语言模型在瑞典语词义消歧任务上的表现，使用了两个基准测试集。目前，在存在可用训练集的情况下，所有现有模型的准确率均低于最优的有监督消歧系统；但多数模型的表现优于基于图结构的无监督系统。研究比较了不同的提示策略，重点关注如何在给定语境中表达可能的词义集合。实验结果表明，当提示中包含人工撰写的词义定义时，模型能够达到最高的消歧准确率。