@inproceedings{yu-2025-towards,
    title = "Towards Comprehensive Evaluation of Open-Source Language Models: A Multi-Dimensional, User-Driven Approach",
    author = "Yu, Qingchen",
    editor = "Arviv, Ofir  and
      Clinciu, Miruna  and
      Dhole, Kaustubh  and
      Dror, Rotem  and
      Gehrmann, Sebastian  and
      Habba, Eliya  and
      Itzhak, Itay  and
      Mille, Simon  and
      Perlitz, Yotam  and
      Santus, Enrico  and
      Sedoc, Jo{\~a}o  and
      Shmueli Scheuer, Michal  and
      Stanovsky, Gabriel  and
      Tafjord, Oyvind",
    booktitle = "Proceedings of the Fourth Workshop on Generation, Evaluation and Metrics (GEM{\texttwosuperior})",
    month = jul,
    year = "2025",
    address = "Vienna, Austria and virtual meeting",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/nschneid-patch-1/2025.gem-1.1/",
    pages = "1--7",
    ISBN = "979-8-89176-261-9",
    abstract = "With rapid advancements in large language models (LLMs) across artificial intelligence, machine learning, and data sci-ence, there is a growing need for evaluation frameworks that go beyond traditional performance metrics. Conventional methods focus mainly on accuracy and computational metrics, often neglecting user experience and community interaction{---}key elements in open-source environments. This paper intro-duces a multi-dimensional, user-centered evaluation frame-work, integrating metrics like User Engagement Index (UEI), Community Response Rate (CRR), and a Time Weight Factor (TWF) to assess LLMs' real-world impact. Additionally, we propose an adaptive weighting mechanism using Bayesian op-timization to dynamically adjust metric weights for more ac-curate model evaluation. Experimental results confirm that our framework effectively identifies models with strong user engagement and community support, offering a balanced, data-driven approach to open-source LLM evaluation. This frame-work serves as a valuable tool for developers and researchers in selecting and improving open-source models."
}