mlsanityData to 14 Oct 2026

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

arXiv 2505.16107 ↗First posted 22 May 2025cs.CLcited by 2

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Abstract (arXiv)

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 Multiple Programming Languages with large language models for information extraction (abbreviated as MPL), a novel framework that explores the potential of incorporating different PLs in the SFT phase. Additionally, we introduce textttfunction-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.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 31 of its 58 references that are in the map. First posted on arXiv 22 May 2025.

Builds on

31 of 58 references.

References · 31

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19 more
  1. 2024StarCoder 2 and The Stack v2: The Next Generationcited by 502
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  14. 2021Query and Extract: Refining Event Extraction as Type-oriented Binary Decodingcited by 26
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  19. 2023Labels Need Prompts Too: Mask Matching for Natural Language Understanding Taskscited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026ProUIE: A Macro-to-Micro Progressive Learning Method for LLM-based Universal Information Extractioncited by 0
  2. 2026Enhancing Generative Information Extraction with Two-step Validation: A Product Attribute Use Casecited by 0