When One LLM Drools, Multi-LLM Collaboration Rules

• URL: http://arxiv.org/abs/2502.04506v1
• Date: Thu, 06 Feb 2025 21:13:44 GMT
• Title: When One LLM Drools, Multi-LLM Collaboration Rules
• Authors: Shangbin Feng, Wenxuan Ding, Alisa Liu, Zifeng Wang, Weijia Shi, Yike Wang, Zejiang Shen, Xiaochuang Han, Hunter Lang, Chen-Yu Lee, Tomas Pfister, Yejin Choi, Yulia Tsvetkov,
• Abstract summary: We argue for multi-LLM collaboration to better represent the extensive diversity of data, skills, and people.<n>We organize existing multi-LLM collaboration methods into a hierarchy, based on the level of access and information exchange.<n>We envision multi-LLM collaboration as an essential path toward compositional intelligence and collaborative AI development.
• Score: 98.71562711695991
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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