@inproceedings{yin-etal-2026-decentralized,
    title = "Decentralized Arena: Towards Democratic and Scalable Automatic Evaluation of Language Models",
    author = "Yin, Yanbin  and
      Zhou, Kun  and
      Wang, Zhen  and
      Zhang, Xiangdong  and
      Shao, Yifei  and
      Hao, Shibo  and
      Gu, Yi  and
      Liu, Jieyuan  and
      Singla, Somanshu  and
      Liu, Tianyang  and
      Xing, Eric P.  and
      Liu, Zhengzhong  and
      Jin, Haojian  and
      Hu, Zhiting",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.1639/",
    pages = "35453--35469",
    ISBN = "979-8-89176-390-6",
    abstract = "The recent explosion of large language models (LLMs), each with its own general or specialized strengths, makes scalable, reliable benchmarking more urgent than ever. Standard practices nowadays face fundamental trade-offs: closed-ended question-based benchmarks (MMLU) struggle with saturation as newer models emerge, while crowd-sourced leaderboards (Chatbot Arena) rely on costly and slow human judges. Recently, automated methods (LLM-as-a-judge) shed light on the scalability, but risk bias by relying on one or a few ``authority'' models. To tackle these issues, we propose Decentralized Arena (), a fully automated framework leveraging collective intelligence from all LLMs to evaluate each other. It mitigates single-model judge bias by democratic, pairwise evaluation, and remains efficient at scale through two key components: (1) a coarse-to-fine ranking algorithm for fast incremental insertion of new models with sub-quadratic complexity, and (2) an automatic question selection strategy for the construction of new evaluation dimensions. Across extensive experiments across 66 LLMs, attains up to 97{\%} correlation with human judgements, while significantly reducing the cost."
}