Unlocking Instructive In-Context Learning with Tabular Prompting for
  Relational Triple Extraction
Guozheng Li Wenjun Ke Peng Wang Zijie Xu Ke Ji Jiajun Liu Ziyu Shang Qiqing Luo
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.