Papers with multi-LLM collaboration
Modular Pluralism: Pluralistic Alignment via Multi-LLM Collaboration (2024.emnlp-main)
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Shangbin Feng, Taylor Sorensen, Yuhan Liu, Jillian Fisher, Chan Young Park, Yejin Choi, Yulia Tsvetkov
| Challenge: | Existing alignment paradigms for large language models learn an averaged human preference and struggle to model diverse preferences across cultures, demographics, and communities. |
| Approach: | They propose a modular framework that "plugs" into a base LLM a pool of smaller but specialized community LMs where models collaborate in distinct modes to support three modes of pluralism: Overton, steerable, and distributional. |
| Outcome: | The proposed framework “plugs into” a base LLM a pool of smaller but specialized community LMs, where models collaborate in distinct modes to support three modes of pluralism: Overton, steerable, and distributional. |
BILLY: Steering Large Language Models via Merging Persona Vectors for Creative Generation (2026.eacl-long)
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| Challenge: | Multi-LLM systems enhance creativity of large language models by simulating human collective intelligence but suffer from significant drawbacks, such as high computational costs and inference latency. |
| Approach: | They propose a training-free framework that captures the benefits of multi-LLM collaboration by extracting and blending multiple distinct persona vectors directly in the model’s activation space. |
| Outcome: | The proposed framework surpasses model prompting and traditional multi-LLM approaches while significantly reducing inference time and computational costs. |
Aligning LLMs with Individual Preferences via Interaction (2025.coling-main)
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| Challenge: | Existing studies on LLMs alignment focus on generalizing their behavior to generalized values such as helpfulness, harmlessness, and honesty. |
| Approach: | They train large language models to "interact to align" to implicitly infer user preferences . they use a multi-turn preference dataset to generate a personalized alignment . |
| Outcome: | The proposed method enables dynamic, personalized alignment via interaction with a multi-turn preference dataset. |
When One LLM Drools, Multi-LLM Collaboration Rules (2026.acl-long)
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Shangbin Feng, Wenxuan Ding, Alisa Liu, Zifeng Wang, Weijia Shi, Yike Wang, Shannon Zejiang Shen, Xiaochuang Han, Hunter Lang, Chen-Yu Lee, Tomas Pfister, Yejin Choi, Yulia Tsvetkov
| Challenge: | a single general-purpose LLM is not enough to produce a reliable output, argues this paper . a multi-LLM collaboration approach addresses reliability, democratization, and pluralism . |
| Approach: | They argue that a single general-purpose LLM is not enough to produce a reliable output . they organize existing multi-LLM collaboration methods into a hierarchy based on access and information exchange . |
| Outcome: | The proposed method addresses reliability, democratization, and pluralism challenges a single LLM fails to produce a reliable output. |