When One LLM Drools, Multi-LLM Collaboration Rules
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arXiv
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| Hauptverfasser: | , , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866912223236980736 |
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| author | Feng, Shangbin Ding, Wenxuan Liu, Alisa Wang, Zifeng Shi, Weijia Wang, Yike Shen, Zejiang Han, Xiaochuang Lang, Hunter Lee, Chen-Yu Pfister, Tomas Choi, Yejin Tsvetkov, Yulia |
| author_facet | Feng, Shangbin Ding, Wenxuan Liu, Alisa Wang, Zifeng Shi, Weijia Wang, Yike Shen, Zejiang Han, Xiaochuang Lang, Hunter Lee, Chen-Yu Pfister, Tomas Choi, Yejin Tsvetkov, Yulia |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_04506 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | When One LLM Drools, Multi-LLM Collaboration Rules Feng, Shangbin Ding, Wenxuan Liu, Alisa Wang, Zifeng Shi, Weijia Wang, Yike Shen, Zejiang Han, Xiaochuang Lang, Hunter Lee, Chen-Yu Pfister, Tomas Choi, Yejin Tsvetkov, Yulia Computation and Language 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. |
| title | When One LLM Drools, Multi-LLM Collaboration Rules |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2502.04506 |