在总量最大化中对大型语言模型进行零射击性能分析
Ali Abir Shuvro1, Md Shahriar Islam Bhuiyan1, Faisal Hussain1
1Department of Computer Science and Engineering, Islamic University of Technology, Gazipur, Dhaka, Bangladesh.
大型语言模型 (LLM) 显示了网络资源优化的潜力,在总量最大化任务中实现了58%的效率. 然而,目前的LLM变体需要微调以获得数字数据的最佳性能.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 电信工程 电信工程 电信工程
背景情况:
- 大型语言模型 (LLM) 越来越多样化,影响包括自然语言处理在内的多个领域.
- 在网络中,LLM为资源优化和共享提供了潜力,特别是在诸如总量最大化等复杂任务中.
- 对于总量最大化的传统算法可能很难实现和理解.
研究的目的:
- 评估在没有先前算法知识的情况下使用LLM用于网络中的总量最大化的可行性.
- 评估LLM在网络资源优化的零射击环境中的性能.
- 为了比较LLM的表现与总量最大化最先进的方法.
主要方法:
- 对LLM进行了零射击分析,以评估它们适合于总量最大化.
- 实验包括改变蜂用户数量和设备对设备 (D2D) 配对.
- 对LLM的效率进行了测量,并与已建立的总量最大化方法进行了比较.
主要成果:
- 对于总量最大化,GPT模型实现了大约58%的最大平均效率.
- 性能根据测试的蜂用户和D2D对的组合而异.
- 一些LLM变体在当前状态下表现出数值和结构数据的限制.
结论:
- 在网络中,LLM,特别是GPT,对简化总量最大化有很大的希望.
- 进一步的研究和参数微调是必要的,以实现对数值网络数据的最佳LLM应用.
- 实际上,LLM可以降低与实施先进的网络算法相关的复杂性.
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