Large Language Models (LLMs) have achieved remarkable success in many formal language oriented tasks, such as structural data-to-text and semantic parsing. However current benchmarks mostly follow the data distribution of the pre-training data of LLMs. Therefore, a natural question rises that do LLMs really understand the structured semantics of formal languages. In this paper, we investigate this problem on a special case, converse binary relation. We introduce a new benchmark ConvRe focusing on converse relations, which contains 17 relations and 1240 triples extracted from popular knowledge graph completion datasets. Our ConvRE features two tasks, Re2Text and Text2Re, which are formulated as multi-choice question answering to evaluate LLMs' ability to determine the matching between relations and associated text. For the evaluation protocol, apart from different prompting methods, we further introduce variants to the test text and few-shot example text. We conduct experiments on three popular LLM families and have observed various scaling trends. The results suggest that LLMs often resort to shortcut learning and still face challenges on our proposed benchmark.

翻译：大语言模型（LLMs）在许多面向形式化语言的任务中取得了显著成功，例如结构化数据到文本生成和语义解析。然而，当前基准测试大多遵循LLMs预训练数据的数据分布。因此，一个自然的问题随之产生：LLMs是否真正理解形式化语言的结构化语义？在本文中，我们以二元反向关系为特例研究这一问题。我们引入了一个专注于反向关系的新基准ConvRe，该基准包含从主流知识图谱补全数据集中提取的17个关系和1240个三元组。我们的ConvRe设计了两个任务：Re2Text和Text2Re，它们被构建为多项选择问答形式，用于评估LLMs在关系与相关文本之间进行匹配的能力。在评估方案上，除了不同的提示方法外，我们还对测试文本和少量样本示例文本引入了变体。我们在三个主流LLM系列上进行了实验，并观察到多种缩放趋势。结果表明，LLMs常依赖捷径学习，且在我们提出的基准上仍面临挑战。