COSTAR-A:增强LLM在视角问题上表现的提示框架论文信息
标题: COSTAR-A: A prompting framework for enhancing Large Language Model performance on Point-of-View questions
作者: Nzubechukwu C. Ohalete, Kevin B. Gittner, Lauren M. Matheny
发布日期: 2025-10-14
ArXiv链接: https://arxiv.org/abs/2510.12637
核心概述大型语言模型(LLM)对提示设计高度敏感,制定优化的提示技术对于生成一致的高质量输出至关重要。本研究引入COSTAR-A,一种增强现有COSTAR方法的新型提示工程框架。该框架特别针对视角(Point-of-View)问题进行了优化,这类问题要求模型从特...
突破记忆墙:长上下文代理LLM推理的优化路径
突破记忆墙:长上下文代理LLM推理的优化路径论文信息
标题: Combating the Memory Walls: Optimization Pathways for Long-Context Agentic LLM Inference
作者: Haoran Wu, Can Xiao, Jiayi Nie, Xuan Guo, Binglei Lou, Jeffrey T. H. Wong, Zhiwen Mo, Cheng Zhang, Przemyslaw Forys, Wayne Luk, Hongxiang Fan, Jianyi Cheng, Timothy M. Jones, Rika Antonova, Robert Mullins, Aaron Zhao
发布日期: 2025-09-11
ArXiv链接: https://arxiv.org/abs/2509.095...