@inproceedings{zhao-etal-2025-redone,
title = "{R}ed{O}ne: Revealing Domain-specific {LLM} Post-Training in Social Networking Services",
author = "Zhao, Fei and
Lu, Chonggang and
Wangyue and
Xie, Zheyong and
Liu, Ziyan and
Qian, Haofu and
Huang, Jianzhao and
Shi, Fangcheng and
Meng, Zijie and
Guo, Hongcheng and
He, Mingqian and
Lyu, Xinze and
Ye, Zheyu and
Liu, Weiting and
Wang, Boyang and
Cao, Shaosheng",
editor = "Potdar, Saloni and
Rojas-Barahona, Lina and
Montella, Sebastien",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track",
month = nov,
year = "2025",
address = "Suzhou (China)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-industry.180/",
pages = "2648--2674",
ISBN = "979-8-89176-333-3",
abstract = "As a primary medium for modern information dissemination, social networking services (SNS) have experienced rapid growth, which has proposed significant challenges for platform content management and interaction quality improvement. Recently, the development of large language models (LLMs) has offered potential solutions but existing studies focus on isolated tasks, which not only encounter diminishing benefit from the data scaling within individual scenarios but also fail to flexibly adapt to diverse real-world context. To address these challenges, we introduce RedOne, a domain-specific LLM designed to break the performance bottleneck of single-task baselines and establish a comprehensive foundation for the SNS. RedOne was developed through a three-stage training strategy consisting of continue pretraining, supervised fine-tuning, and preference optimization, using a large-scale real-world dataset. Through extensive experiments, RedOne maintains strong general capabilities, and achieves an average improvement up to 14.02{\%} across 8 major SNS tasks and 7.56{\%} in SNS bilingual evaluation benchmark, compared with base models. Furthermore, through online testing, RedOne reduced the exposure rate in harmful content detection by 11.23{\%} and improved the click page rate in post-view search by 14.95{\%} compared with single-tasks baseline models. These results establish RedOne as a robust domain-specific LLM for SNS, demonstrating excellent generalization across various tasks and promising applicability in real-world scenarios."
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<abstract>As a primary medium for modern information dissemination, social networking services (SNS) have experienced rapid growth, which has proposed significant challenges for platform content management and interaction quality improvement. Recently, the development of large language models (LLMs) has offered potential solutions but existing studies focus on isolated tasks, which not only encounter diminishing benefit from the data scaling within individual scenarios but also fail to flexibly adapt to diverse real-world context. To address these challenges, we introduce RedOne, a domain-specific LLM designed to break the performance bottleneck of single-task baselines and establish a comprehensive foundation for the SNS. RedOne was developed through a three-stage training strategy consisting of continue pretraining, supervised fine-tuning, and preference optimization, using a large-scale real-world dataset. Through extensive experiments, RedOne maintains strong general capabilities, and achieves an average improvement up to 14.02% across 8 major SNS tasks and 7.56% in SNS bilingual evaluation benchmark, compared with base models. Furthermore, through online testing, RedOne reduced the exposure rate in harmful content detection by 11.23% and improved the click page rate in post-view search by 14.95% compared with single-tasks baseline models. These results establish RedOne as a robust domain-specific LLM for SNS, demonstrating excellent generalization across various tasks and promising applicability in real-world scenarios.</abstract>
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%0 Conference Proceedings
%T RedOne: Revealing Domain-specific LLM Post-Training in Social Networking Services
%A Zhao, Fei
%A Lu, Chonggang
%A Xie, Zheyong
%A Liu, Ziyan
%A Qian, Haofu
%A Huang, Jianzhao
%A Shi, Fangcheng
%A Meng, Zijie
%A Guo, Hongcheng
%A He, Mingqian
%A Lyu, Xinze
%A Ye, Zheyu
%A Liu, Weiting
%A Wang, Boyang
%A Cao, Shaosheng
%Y Potdar, Saloni
%Y Rojas-Barahona, Lina
%Y Montella, Sebastien
%A Wangyue
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou (China)
%@ 979-8-89176-333-3
%F zhao-etal-2025-redone
%X As a primary medium for modern information dissemination, social networking services (SNS) have experienced rapid growth, which has proposed significant challenges for platform content management and interaction quality improvement. Recently, the development of large language models (LLMs) has offered potential solutions but existing studies focus on isolated tasks, which not only encounter diminishing benefit from the data scaling within individual scenarios but also fail to flexibly adapt to diverse real-world context. To address these challenges, we introduce RedOne, a domain-specific LLM designed to break the performance bottleneck of single-task baselines and establish a comprehensive foundation for the SNS. RedOne was developed through a three-stage training strategy consisting of continue pretraining, supervised fine-tuning, and preference optimization, using a large-scale real-world dataset. Through extensive experiments, RedOne maintains strong general capabilities, and achieves an average improvement up to 14.02% across 8 major SNS tasks and 7.56% in SNS bilingual evaluation benchmark, compared with base models. Furthermore, through online testing, RedOne reduced the exposure rate in harmful content detection by 11.23% and improved the click page rate in post-view search by 14.95% compared with single-tasks baseline models. These results establish RedOne as a robust domain-specific LLM for SNS, demonstrating excellent generalization across various tasks and promising applicability in real-world scenarios.
%U https://aclanthology.org/2025.emnlp-industry.180/
%P 2648-2674
Markdown (Informal)
[RedOne: Revealing Domain-specific LLM Post-Training in Social Networking Services](https://aclanthology.org/2025.emnlp-industry.180/) (Zhao et al., EMNLP 2025)
ACL
- Fei Zhao, Chonggang Lu, Wangyue, Zheyong Xie, Ziyan Liu, Haofu Qian, Jianzhao Huang, Fangcheng Shi, Zijie Meng, Hongcheng Guo, Mingqian He, Xinze Lyu, Zheyu Ye, Weiting Liu, Boyang Wang, and Shaosheng Cao. 2025. RedOne: Revealing Domain-specific LLM Post-Training in Social Networking Services. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 2648–2674, Suzhou (China). Association for Computational Linguistics.