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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

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

针对鱼优化的LSTM网络,用于增强的自动文本总结.

Bharathi Mohan Gurusamy¹, Prasanna Kumar Rangarajan¹, Ali Altalbe^2,3

¹Department of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Chennai, India.

Frontiers in artificial intelligence

|September 13, 2024

概括

本研究介绍了一种优化自动编码的长期短期记忆网络 (OAELSTM) 与鱼优化算法 (WOA) 进行更好的自动文本总结. 与现有方法相比,OAELSTM模型产生了更连贯和更具信息性的摘要.

关键词:

自动编码自动编码这是LSTM的LSTM.鱼优化算法鱼优化算法优化的优化优化优化.总结总结总结总结

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科学领域:

自然语言处理自然语言处理.
人工智能的人工智能
机器学习机器学习

背景情况:

当前的自动文本总结方法往往无法保持上下文完整性和捕捉微妙的句子关系.
现有的模型经常产生通用或不连接的摘要,丢失关键信息.

研究的目的:

引入一种使用优化自动编码长短期记忆网络 (OAELSTM) 进行自动文本总结的新方法.
通过鱼优化算法 (WOA) 提高OAELSTM模型的性能和效率.
提高生成的摘要的连贯性,信息性和上下文完整性.

主要方法:

实施深度LSTM网络,集成自动编码机制,用于关键词和概念提取.
使用鱼优化算法 (WOA) 微调OAELSTM模型参数.
基于基准数据集的评估,例如CNN/Daily Mail和Gigaword.

主要成果:

与现有的总结方法相比,OAELSTM模型表现出更高的性能.
获得了0.456的ROUGE得分,准确率为84.47%,特异性得分为0.3244.
该模型在4341.95秒的时间内高效地处理数据.

结论:

由WOA增强的OAELSTM模型在自动文本总结方面取得了显著的进步.
提出的方法有效地生成信息丰富和连贯的摘要,同时保持上下文完整性.
该模型的性能指标表明其在自然语言处理任务中的实际应用潜力.