用多种编程语言提升信息抽取的结构化生成能力
MPL: Multiple Programming Languages with Large Language Models for Information Extraction
- 引入多种编程语言替代单一Python，增强模型结构化输出能力
- 在多个数据集上表现优于单语言方法，验证多语言有效性
- 适合希望提升信息抽取精度的研究者和工业应用开发者
近期信息抽取（IE）研究关注使用代码风格输入以提升结构化输出生成效果。其核心思想是编程语言（PLs）相比自然语言（NLs）具有更强的结构性，更适合作为IE任务的输入形式。然而，现有工作大多仅使用Python进行代码风格模拟，忽略了其他广泛使用的编程语言（如C++、Java）在监督微调（SFT）阶段的潜力。本文提出一种新框架MPL（Multiple Programming Languages with Large Language Models for Information Extraction），探索在SFT阶段融合多种编程语言的可能性。同时，我们设计了带有虚拟运行功能的 exttt{function-prompt}，更高效地模拟代码风格输入。在多个数据集上的实验结果表明，MPL显著有效。我们还进行了全面的实验分析，并已开源代码供后续研究使用。
原文摘要 · Abstract (English)
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.
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