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Collaborate large and small language models for multi-modal emergency rumor detection.

Youcheng Yan1, Jinshuo Liu1, Juan Deng2

  • 1School of Cyber Science and Engineering, Wuhan University, 299 Bayi road, Wuhan, 430072, China.

Neural Networks : the Official Journal of the International Neural Network Society
|June 11, 2025
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Summary
This summary is machine-generated.

This study introduces a novel approach for multi-modal emergency rumor detection, enhancing accuracy by integrating large language models (LLMs) and small language models (SLMs) for better analysis and fusion of text and image data.

Keywords:
Large language modelMulti-modal fusionNetwork content securityRumor detectionSmall language model

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Area of Science:

  • Artificial Intelligence
  • Natural Language Processing
  • Computer Vision

Background:

  • Multi-modal emergency rumors pose significant societal challenges.
  • Existing rumor detection models using only SLMs or LLMs have limitations in knowledge capacity and analytical integration.
  • Current multi-modal fusion techniques are often superficial, hindering comprehensive rumor identification.

Purpose of the Study:

  • To propose an effective method for multi-modal emergency rumor detection.
  • To address the limitations of individual SLM and LLM approaches.
  • To improve the fusion of textual and visual information for accurate rumor identification.

Main Methods:

  • Developed Collaborate Large and Small Language Models for Multi-Modal Emergency Rumor Detection (M2ERD).
  • LLMs generate multi-dimensional rationales; SLMs derive insights for detection.
  • Implemented a multi-source cross-modal penetration fusion network for text-image complementation.

Main Results:

  • M2ERD achieved a 2.6% accuracy improvement and a 1.9% F1-score improvement over baselines.
  • Demonstrated effectiveness on Weibo, RumorEval, and Pheme datasets.
  • The proposed method shows superior performance in multi-modal rumor detection.

Conclusions:

  • The M2ERD framework effectively enhances multi-modal emergency rumor detection.
  • Collaborative LLM-SLM approach and advanced fusion network improve analytical capabilities.
  • The study provides a significant advancement in combating the spread of misinformation.