@inproceedings{wang-etal-2025-rewardds,
title = "{R}eward{DS}: Privacy-Preserving Fine-Tuning for Large Language Models via Reward Driven Data Synthesis",
author = "Wang, Jianwei and
Shi, Chengming and
Yang, Junyao and
Li, Haoran and
Ma, Qianli and
Zhuang, Huiping and
Chen, Cen and
Zeng, Ziqian",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.223/",
doi = "10.18653/v1/2025.emnlp-main.223",
pages = "4479--4500",
ISBN = "979-8-89176-332-6",
abstract = "The success of large language models (LLMs) has attracted many individuals to fine-tune them for domain-specific tasks by uploading their data. However, in sensitive areas like healthcare and finance, privacy concerns often arise. One promising solution is to generate synthetic data with Differential Privacy (DP) guarantees to replace private data. However, these synthetic data contain significant flawed data, which are considered as noise. Existing solutions typically rely on naive filtering by comparing ROUGE-L scores or embedding similarities, which are ineffective in addressing the noise. To address this issue, we propose ***RewardDS***, a novel privacy-preserving framework that fine-tunes a reward proxy model and uses reward signals to guide the synthetic data generation. Our RewardDS introduces two key modules, Reward Guided Filtering and Self-Optimizing Refinement, to both filter and refine the synthetic data, effectively mitigating the noise. Extensive experiments across medical, financial, and code generation domains demonstrate the effectiveness of our method."
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<abstract>The success of large language models (LLMs) has attracted many individuals to fine-tune them for domain-specific tasks by uploading their data. However, in sensitive areas like healthcare and finance, privacy concerns often arise. One promising solution is to generate synthetic data with Differential Privacy (DP) guarantees to replace private data. However, these synthetic data contain significant flawed data, which are considered as noise. Existing solutions typically rely on naive filtering by comparing ROUGE-L scores or embedding similarities, which are ineffective in addressing the noise. To address this issue, we propose ***RewardDS***, a novel privacy-preserving framework that fine-tunes a reward proxy model and uses reward signals to guide the synthetic data generation. Our RewardDS introduces two key modules, Reward Guided Filtering and Self-Optimizing Refinement, to both filter and refine the synthetic data, effectively mitigating the noise. Extensive experiments across medical, financial, and code generation domains demonstrate the effectiveness of our method.</abstract>
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%0 Conference Proceedings
%T RewardDS: Privacy-Preserving Fine-Tuning for Large Language Models via Reward Driven Data Synthesis
%A Wang, Jianwei
%A Shi, Chengming
%A Yang, Junyao
%A Li, Haoran
%A Ma, Qianli
%A Zhuang, Huiping
%A Chen, Cen
%A Zeng, Ziqian
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F wang-etal-2025-rewardds
%X The success of large language models (LLMs) has attracted many individuals to fine-tune them for domain-specific tasks by uploading their data. However, in sensitive areas like healthcare and finance, privacy concerns often arise. One promising solution is to generate synthetic data with Differential Privacy (DP) guarantees to replace private data. However, these synthetic data contain significant flawed data, which are considered as noise. Existing solutions typically rely on naive filtering by comparing ROUGE-L scores or embedding similarities, which are ineffective in addressing the noise. To address this issue, we propose ***RewardDS***, a novel privacy-preserving framework that fine-tunes a reward proxy model and uses reward signals to guide the synthetic data generation. Our RewardDS introduces two key modules, Reward Guided Filtering and Self-Optimizing Refinement, to both filter and refine the synthetic data, effectively mitigating the noise. Extensive experiments across medical, financial, and code generation domains demonstrate the effectiveness of our method.
%R 10.18653/v1/2025.emnlp-main.223
%U https://aclanthology.org/2025.emnlp-main.223/
%U https://doi.org/10.18653/v1/2025.emnlp-main.223
%P 4479-4500
Markdown (Informal)
[RewardDS: Privacy-Preserving Fine-Tuning for Large Language Models via Reward Driven Data Synthesis](https://aclanthology.org/2025.emnlp-main.223/) (Wang et al., EMNLP 2025)
ACL
- Jianwei Wang, Chengming Shi, Junyao Yang, Haoran Li, Qianli Ma, Huiping Zhuang, Cen Chen, and Ziqian Zeng. 2025. RewardDS: Privacy-Preserving Fine-Tuning for Large Language Models via Reward Driven Data Synthesis. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 4479–4500, Suzhou, China. Association for Computational Linguistics.