@inproceedings{feng-etal-2026-one,
    title = "When One {LLM} Drools, Multi-{LLM} Collaboration Rules",
    author = "Feng, Shangbin  and
      Ding, Wenxuan  and
      Liu, Alisa  and
      Wang, Zifeng  and
      Shi, Weijia  and
      Wang, Yike  and
      Shen, Shannon Zejiang  and
      Han, Xiaochuang  and
      Lang, Hunter  and
      Lee, Chen-Yu  and
      Pfister, Tomas  and
      Choi, Yejin  and
      Tsvetkov, Yulia",
    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.775/",
    pages = "17048--17063",
    ISBN = "979-8-89176-390-6",
    abstract = "This position paper argues that in many realistic (i.e., complex, contextualized, subjective) scenarios, one LLM is not enough to produce a reliable output. We challenge the status quo of relying solely on a single general-purpose LLM and argue for multi-LLM collaboration to better represent the extensive diversity of data, skills, and people. We first posit that a single LLM underrepresents real-world data distributions, heterogeneous skills, and pluralistic populations, and that such representation gaps cannot be trivially patched by further training a single LLM. We then organize existing multi-LLM collaboration methods into a hierarchy, based on the level of access and information exchange, ranging from API-level, text-level, logit-level, to weight-level collaboration. Based on these methods, we highlight how multi-LLM collaboration addresses challenges that a single LLM struggles with, such as reliability, democratization, and pluralism. Finally, we identify the limitations of existing multi-LLM methods and motivate future work. We envision multi-LLM collaboration as an essential path toward compositional intelligence and collaborative AI development."
}