大型语言模型是多任务思维链,促使优化器
IEEE transactions on neural networks and learning systems
|December 30, 2025
概括
本研究引入了一种新的方法来改进大语言模型 (LLM) 推理,通过使用LLM本身来改进提示,显著提高复杂任务的性能.
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.
背景情况:
- 大型语言模型 (LLM) 显示出令人印象深刻的推理能力,但对提示设计非常敏感.
- 目前的提示工程方法,如手动思维链 (CoT) 和自动生成,是劳动密集型的,缺乏通用性.
研究的目的:
- 开发一种更有效,更具普遍性的方法来提高LLM推理性能.
- 为了利用LLMs进行代的提示自我完善和集成高性能提示.
主要方法:
- 将LLM视为一种多任务优化器,用于使用自然语言任务描述和短暂的上下文学习 (ICL) 进行代提示自我改进.
- 在推断时间组合高性能提示,以考虑任务依赖的提示灵敏度.
主要成果:
- 在各种LLM中,与之前的基线相比,实现了显著的绩效增长.
- 在数学推理方面表现出高达6.0%的改进,在常识推理基准方面表现出10.2%.
结论:
- 建议的多任务优化和快速组合方法为传统的快速工程技术提供了强大而高效的替代方案.
- 这种方法显著提高了LLM的推理能力,特别是在数学和常识理解等复杂领域.
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