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相关实验视频

Updated: Jun 13, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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协作大和小的语言模型,用于多模式的紧急谣言检测.

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
PubMed
概括

本研究引入了一种用于多模式紧急谣言检测的新方法,通过整合大语言模型 (LLM) 和小语言模型 (SLM) 来提高准确性,以便更好地分析和融合文本和图像数据.

关键词:
大型语言模型.多模式融合多模式融合网络内容安全 网络内容安全谣言检测 谣言检测 谣言检测小语言模型的小语言模型.

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科学领域:

  • 人工智能的人工智能
  • 自然语言处理自然语言处理.
  • 计算机视觉 计算机视觉

背景情况:

  • 多式联运紧急情况的谣言带来了重大的社会挑战.
  • 目前仅使用SLM或LLM的谣言检测模型在知识能力和分析整合方面存在局限性.
  • 当前的多模融合技术往往是肤浅的,阻碍了全面的谣言识别.

研究的目的:

  • 提出一种有效的方法,用于多模式的紧急谣言检测.
  • 解决单个SLM和LLM方法的局限性.
  • 改进文本和视觉信息的融合,以准确识别谣言.

主要方法:

  • 开发了合作的大型和小型语言模型,用于多模式紧急谣言检测 (M2ERD).
  • 法律法规产生多维的理性;法律法规获得的洞察力用于检测.
  • 实施了多源交叉模式透融合网络,用于文本图像补充.

主要成果:

  • 与基线相比,M2ERD实现了2.6%的精度改善和1.9%的F1得分改善.
  • 在微博,RumorEval和Pheme数据集上证明了有效性.
  • 拟议的方法在多式联络传闻检测方面表现优越.

结论:

  • M2ERD框架有效地增强了多模式的紧急谣言检测.
  • 协作式的LLM-SLM方法和先进的融合网络提高了分析能力.
  • 这项研究在打击错误信息传播方面取得了重大进展.