标题： LLMartini：通过比较和组合实现多个大语言模型的无缝和交互式利用 显示英文标题

标题： LLMartini: Seamless and Interactive Leveraging of Multiple LLMs through Comparison and Composition

摘要： 大型语言模型（LLMs）的日益多样化意味着用户常常需要比较和结合不同模型的输出，以获得更高质量或更全面的响应。 然而，切换到不同的界面并手动整合输出本质上是低效的，导致较高的认知负担和碎片化的工作流程。 为了解决这个问题，我们提出了 LLMartini，一种新型的交互系统，支持无缝比较、选择和直观的跨模型组合工具。 该系统根据任务特定的标准将响应分解为语义对齐的片段，自动合并共识内容，并通过颜色编码突出显示模型差异，同时保留独特的贡献。 在一项用户研究（N=18）中，LLMartini 在所有测量指标上均显著优于传统的手动方法，包括任务完成时间、认知负荷和用户满意度。 我们的工作突出了以人为本的设计在提升多LLM交互效率和创造力中的重要性，并为利用各种语言模型的互补优势提供了实际意义。 显示英文摘要

摘要： The growing diversity of large language models (LLMs) means users often need to compare and combine outputs from different models to obtain higher-quality or more comprehensive responses. However, switching between separate interfaces and manually integrating outputs is inherently inefficient, leading to a high cognitive burden and fragmented workflows. To address this, we present LLMartini, a novel interactive system that supports seamless comparison, selection, and intuitive cross-model composition tools. The system decomposes responses into semantically aligned segments based on task-specific criteria, automatically merges consensus content, and highlights model differences through color coding while preserving unique contributions. In a user study (N=18), LLMartini significantly outperformed conventional manual methods across all measured metrics, including task completion time, cognitive load, and user satisfaction. Our work highlights the importance of human-centered design in enhancing the efficiency and creativity of multi-LLM interactions and offers practical implications for leveraging the complementary strengths of various language models.