The widespread usage of latent language representations via pre-trained language models (LMs) suggests that they are a promising source of structured knowledge. However, existing methods focus only on a single object per subject-relation pair, even though often multiple objects are correct. To overcome this limitation, we analyze these representations for their potential to yield materialized multi-object relational knowledge. We formulate the problem as a rank-then-select task. For ranking candidate objects, we evaluate existing prompting techniques and propose new ones incorporating domain knowledge. Among the selection methods, we find that choosing objects with a likelihood above a learned relation-specific threshold gives a 49.5% F1 score. Our results highlight the difficulty of employing LMs for the multi-valued slot-filling task and pave the way for further research on extracting relational knowledge from latent language representations.

翻译：通过预训练语言模型（LMs）广泛使用潜在语言表示，表明其作为结构化知识的有望来源。然而，现有方法仅关注每个主谓-关系对中的单一对象，尽管通常存在多个正确对象。为克服这一局限，我们分析了这些表示在生成具体化多对象关系知识方面的潜力。我们将问题形式化为先排序后选择的任务。在候选对象排序中，我们评估了现有的提示技术，并提出了融入领域知识的新方法。在选择方法中，我们发现选择概率高于学习到的关系特定阈值的对象可达到49.5%的F1得分。我们的结果突显了将LMs用于多值槽填充任务的困难，并为从潜在语言表示中提取关系知识的进一步研究铺平了道路。