MPL: Multiple Programming Languages with Large Language Models for Information Extraction

TL;DR

This paper introduces MPL, a framework that leverages multiple programming languages and a novel function-prompt technique to improve information extraction with large language models, demonstrating broad effectiveness.

Contribution

MPL explores the use of various programming languages in supervised fine-tuning and introduces function-prompt with virtual running to enhance IE tasks.

Findings

MPL outperforms existing methods across multiple datasets.

Using diverse programming languages improves extraction accuracy.

Function-prompt with virtual running enhances input simulation efficiency.

Abstract

Recent research in information extraction (IE) focuses on utilizing code-style inputs to enhance structured output generation. The intuition behind this is that the programming languages (PLs) inherently exhibit greater structural organization than natural languages (NLs). This structural advantage makes PLs particularly suited for IE tasks. Nevertheless, existing research primarily focuses on Python for code-style simulation, overlooking the potential of other widely-used PLs (e.g., C++ and Java) during the supervised fine-tuning (SFT) phase. In this research, we propose \textbf{M}ultiple \textbf{P}rogramming \textbf{L}anguages with large language models for information extraction (abbreviated as \textbf{MPL}), a novel framework that explores the potential of incorporating different PLs in the SFT phase. Additionally, we introduce \texttt{function-prompt} with virtual running to simulate code-style inputs more effectively and efficiently. Experimental results on a wide range of datasets demonstrate the effectiveness of MPL. Furthermore, we conduct extensive experiments to provide a comprehensive analysis. We have released our code for future research.