@inproceedings{jiao-etal-2025-synergistic,
title = "Synergistic Weak-Strong Collaboration by Aligning Preferences",
author = "Jiao, Yizhu and
Zhang, Xuchao and
Wang, Zhaoyang and
Ma, Yubo and
Deng, Zhun and
Wang, Rujia and
Bansal, Chetan and
Rajmohan, Saravan and
Han, Jiawei and
Yao, Huaxiu",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.995/",
doi = "10.18653/v1/2025.acl-long.995",
pages = "20355--20371",
ISBN = "979-8-89176-251-0",
abstract = "Current Large Language Models excel in general reasoning yet struggle with specialized tasks requiring proprietary or domain-specific knowledge. Fine-tuning large models for every niche application is often infeasible due to black-box constraints and high computational overhead. To address this, we propose a collaborative framework that pairs a specialized weak model with a general strong model. The weak model, tailored to specific domains, produces initial drafts and background information, while the strong model leverages its advanced reasoning to refine these drafts, extending LLMs' capabilities to critical yet specialized tasks. To optimize this collaboration, we introduce a collaborative feedback to fine-tunes the weak model, which quantifies the influence of the weak model{'}s contributions in the collaboration procedure and establishes preference pairs to guide preference tuning of the weak model. We validate our framework through experiments on three domains. We find that the collaboration significantly outperforms each model alone by leveraging complementary strengths. Moreover, aligning the weak model with the collaborative preference further enhances overall performance."
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<abstract>Current Large Language Models excel in general reasoning yet struggle with specialized tasks requiring proprietary or domain-specific knowledge. Fine-tuning large models for every niche application is often infeasible due to black-box constraints and high computational overhead. To address this, we propose a collaborative framework that pairs a specialized weak model with a general strong model. The weak model, tailored to specific domains, produces initial drafts and background information, while the strong model leverages its advanced reasoning to refine these drafts, extending LLMs’ capabilities to critical yet specialized tasks. To optimize this collaboration, we introduce a collaborative feedback to fine-tunes the weak model, which quantifies the influence of the weak model’s contributions in the collaboration procedure and establishes preference pairs to guide preference tuning of the weak model. We validate our framework through experiments on three domains. We find that the collaboration significantly outperforms each model alone by leveraging complementary strengths. Moreover, aligning the weak model with the collaborative preference further enhances overall performance.</abstract>
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%0 Conference Proceedings
%T Synergistic Weak-Strong Collaboration by Aligning Preferences
%A Jiao, Yizhu
%A Zhang, Xuchao
%A Wang, Zhaoyang
%A Ma, Yubo
%A Deng, Zhun
%A Wang, Rujia
%A Bansal, Chetan
%A Rajmohan, Saravan
%A Han, Jiawei
%A Yao, Huaxiu
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F jiao-etal-2025-synergistic
%X Current Large Language Models excel in general reasoning yet struggle with specialized tasks requiring proprietary or domain-specific knowledge. Fine-tuning large models for every niche application is often infeasible due to black-box constraints and high computational overhead. To address this, we propose a collaborative framework that pairs a specialized weak model with a general strong model. The weak model, tailored to specific domains, produces initial drafts and background information, while the strong model leverages its advanced reasoning to refine these drafts, extending LLMs’ capabilities to critical yet specialized tasks. To optimize this collaboration, we introduce a collaborative feedback to fine-tunes the weak model, which quantifies the influence of the weak model’s contributions in the collaboration procedure and establishes preference pairs to guide preference tuning of the weak model. We validate our framework through experiments on three domains. We find that the collaboration significantly outperforms each model alone by leveraging complementary strengths. Moreover, aligning the weak model with the collaborative preference further enhances overall performance.
%R 10.18653/v1/2025.acl-long.995
%U https://aclanthology.org/2025.acl-long.995/
%U https://doi.org/10.18653/v1/2025.acl-long.995
%P 20355-20371
Markdown (Informal)
[Synergistic Weak-Strong Collaboration by Aligning Preferences](https://aclanthology.org/2025.acl-long.995/) (Jiao et al., ACL 2025)
ACL
- Yizhu Jiao, Xuchao Zhang, Zhaoyang Wang, Yubo Ma, Zhun Deng, Rujia Wang, Chetan Bansal, Saravan Rajmohan, Jiawei Han, and Huaxiu Yao. 2025. Synergistic Weak-Strong Collaboration by Aligning Preferences. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 20355–20371, Vienna, Austria. Association for Computational Linguistics.