To increase the provision of rural public goods is a major task of Chinese new rural construction, while it can not do without the necessary supervision. This paper described the relationship between public choice the...To increase the provision of rural public goods is a major task of Chinese new rural construction, while it can not do without the necessary supervision. This paper described the relationship between public choice theory and the provision of rural public goods and it was a basis for in-depth study of the government＇s subject behavior from the perspective of public choice, and led to the necessity of regulation of rural public goods provision in rural areas, then it presented a strengthen monitoring of the rationalization proposals.展开更多
为解决现有算法选择方法需要复杂流程和专业知识的问题,提出了一种基于大语言模型的强化学习策略。该方法通过参数高效微调对大语言模型进行初始化,为后续的强化学习训练提供高质量的基础。利用指导策略对微调后的模型进行强化学习训练...为解决现有算法选择方法需要复杂流程和专业知识的问题,提出了一种基于大语言模型的强化学习策略。该方法通过参数高效微调对大语言模型进行初始化,为后续的强化学习训练提供高质量的基础。利用指导策略对微调后的模型进行强化学习训练,完成算法选择任务。实验结果表明,在图形类、回归类和控制图类3个场景中,AS-LLM(algorithm selection-large language model)的准确率分别比其它方法的平均准确率高2.23、6.22和5.57个百分点。该方法显著提升了算法选择性能和有效性,且更易于用户操作。展开更多
文摘To increase the provision of rural public goods is a major task of Chinese new rural construction, while it can not do without the necessary supervision. This paper described the relationship between public choice theory and the provision of rural public goods and it was a basis for in-depth study of the government＇s subject behavior from the perspective of public choice, and led to the necessity of regulation of rural public goods provision in rural areas, then it presented a strengthen monitoring of the rationalization proposals.
文摘为解决现有算法选择方法需要复杂流程和专业知识的问题,提出了一种基于大语言模型的强化学习策略。该方法通过参数高效微调对大语言模型进行初始化,为后续的强化学习训练提供高质量的基础。利用指导策略对微调后的模型进行强化学习训练,完成算法选择任务。实验结果表明,在图形类、回归类和控制图类3个场景中,AS-LLM(algorithm selection-large language model)的准确率分别比其它方法的平均准确率高2.23、6.22和5.57个百分点。该方法显著提升了算法选择性能和有效性,且更易于用户操作。
