Unlocking Instructive In-Context Learning with Tabular Prompting for Relational Triple Extraction

TL;DR

This paper introduces a novel tabular prompting method and instructive sample selection strategy to improve in-context learning for relational triple extraction, addressing prompt design and sample selection challenges.

Contribution

It proposes a tabular prompting framework and an instructive in-context learning approach that incorporate structured information and semantic sample selection for better RTE performance.

Findings

Enhanced extraction accuracy with tabular prompting.

Effective sample selection considering triple semantics.

Improved ICL performance over existing methods.

Abstract

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.