The in-context learning (ICL) for relational triple extraction (RTE) has achieved promising performance, but still encounters two key challenges: (1) how to design effective prompts and (2) how to select proper demonstrations. Existing methods, however, fail to address these challenges appropriately. On the one hand, they usually recast RTE task to text-to-text prompting formats, which is unnatural and results in a mismatch between the output format at the pre-training time and the inference time for large language models (LLMs). On the other hand, they only utilize surface natural language features and lack consideration of triple semantics in sample selection. These issues are blocking improved performance in ICL for RTE, thus we aim to tackle prompt designing and sample selection challenges simultaneously. To this end, we devise a tabular prompting for RTE (\textsc{TableIE}) which frames RTE task into a table generation task to incorporate explicit structured information into ICL, facilitating conversion of outputs to RTE structures. Then we propose instructive in-context learning (I$^2$CL) which only selects and annotates a few samples considering internal triple semantics in massive unlabeled samples.

翻译：上下文学习（ICL）在关系三元组抽取（RTE）任务中已取得显著成效，但仍面临两个关键挑战：（1）如何设计有效的提示模板；（2）如何选择恰当的示例样本。然而，现有方法未能妥善解决这些挑战。一方面，它们通常将RTE任务重构为文本到文本的提示格式，这种不自然的转换导致大语言模型（LLMs）在预训练阶段与推理阶段的输出格式不匹配。另一方面，这些方法仅利用表面自然语言特征进行样本选择，缺乏对三元组语义的考量。这些问题阻碍了ICL在RTE任务中的性能提升，因此我们旨在同时解决提示设计与样本选择两大挑战。为此，我们提出面向RTE的表格提示方法（\textsc{TableIE}），将RTE任务转化为表格生成任务，将显式结构化信息融入ICL框架，便于输出结果向RTE结构的转换。进而，我们提出指令式上下文学习（I$^2$CL）方法，该方法仅需对海量未标注样本中少数包含内部三元组语义的样本进行选择与标注。