{"ID":2851513,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.19252","arxiv_id":"2510.19252","title":"LLMartini: Seamless and Interactive Leveraging of Multiple LLMs through Comparison and Composition","abstract":"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.","short_abstract":"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 cogniti...","url_abs":"https://arxiv.org/abs/2510.19252","url_pdf":"https://arxiv.org/pdf/2510.19252v1","authors":"[\"Yingtian Shi\",\"Jinda Yang\",\"Yuhan Wang\",\"Yiwen Yin\",\"Haoyu Li\",\"Kunyu Gao\",\"Chun Yu\"]","published":"2025-10-22T05:12:23Z","proceeding":"cs.HC","tasks":"[\"cs.HC\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}