摘要
信息检索是计算机科学领域的热门研究课题之一,具有广泛的应用场景。但主要局限于特定领域或特定媒介形式,无法满足不同用户群体在不同领域信息交流中与不同媒介的交互需求。针对跨领域信息检索任务,本文提出了一种基于多模态生成式大模型的方法。首先,详细分析了跨领域信息检索技术,总结了信息检索特点,阐述了信息检索方法和检索流程。然后,提出了基于多模态生成的跨领域信息检索模型。最后,通过实验结果验证了算法模型的有效性和可靠性。结果表明,本文提出的方法在多模态大模型中具有较高的准确率,在跨领域信息检索任务中取得了最优的效果。
Information retrieval is one of the hot research topics in the field of computer science,and it has a wide range of application scenarios.However,it is mainly limited to specific domains or medium,so it’s unable meet the interaction needs of different user groups with different media.Aiming at the task of cross-domain information retrieval,this paper proposes a method based on a multimodal generative large model.Firstly,it analyzes the cross-domain information retrieval technology in detail,summarizes the characteristics of information retrieval,and elaborates on the retrieval methods and processes.Then,a cross-domain information retrieval model based on multimodal generation is proposed.Finally,the effectiveness and reliability of the algorithm model are verified through experimental results.The results show that the proposed method achieves high accuracy in multimodal large models and achieves the best performance in cross-domain information retrieval tasks.
作者
余夏萌
黎金婷
肖伟鹏
胡永康
陈新欣
倪元相
YU Xiameng;LI Jinting;XIAO Weipeng;HU Yongkang;CHEN Xinxin;NI Yuanxiang(GuangDong Technology College of Electrical and Electronic Engineering,Zhaoqing 526110,China)
出处
《高科技与产业化》
2025年第9期11-13,共3页
High-Technology & Commercialization
基金
广东理工学院华为ICT产业学院项目,编号:CYXY202201
广东理工学院2024年度教学成果奖培育项目,编号:CGPY202402
2025年国家级大学生创新训练项目—基于跨领域知识图谱重构检索算法提升生成式问答精度的研究,项目编号:202513720002。
关键词
跨领域信息检索
多模态生成式大模型
文本预训练
跨领域交叉注意力机制
cross-domain information retrieval
multimodal generative large model
text pre-training
cross-domain cross-attention mechanism
