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State of Relation Extraction using Large Language Models

Overview

This repository contains a comprehensive survey of recent advancements in Relation Extraction (RE) using Large Language Models (LLMs). The paper explores cutting-edge techniques and methodologies that are transforming the field of information extraction.

Key Contributions

The research provides an in-depth analysis of three primary approaches to relation extraction with LLMs:

  1. Prompt Design

    • Explores techniques like Chain of Thought (CoT)
    • Demonstrates how carefully crafted prompts can improve model performance

  2. Alignment Techniques

    • Addresses challenges in low-incidence tasks
    • Introduces innovative approaches like QA4RE and RAG4RE
    • Shows how reformulating relation extraction can unlock LLM capabilities

  3. Universal Information Extraction (UIE)

    • Proposes a unified framework for information extraction tasks
    • Aims to break down silos between different information extraction approaches

Key Findings

  • Significant performance improvements across multiple benchmarks
  • Successful techniques for addressing LLM limitations in relation extraction
  • Promising directions for future research in information extraction

Datasets Explored

  • DocRED
  • TACRED
  • New York Times Annotated Corpus
  • CoNLL04
  • ACE 2005

Future Research Directions

  • Improved pre-training strategies
  • Multilingual dataset integration
  • Document-level relation extraction
  • Knowledge base-aware models
  • Unified information extraction frameworks

Citation

If you use this work in your research, please cite the original paper:

@article{Sachan2023RelationExtraction,
  title={State of relation extraction using LLMs: A report},
  author={Vangmay Sachan and Yanfei Dong},
  year={2023}
}

Acknowledgements

This research was conducted as part of the Odyssey 2023/2024 program at the National University of Singapore.

Feel free to contact me for further information!