PrivacyMind: Large Language Models Can Be Contextual Privacy Protection Learners
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arXiv
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| Autores principales: | , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2023
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866929561503006720 |
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| author | Xiao, Yijia Jin, Yiqiao Bai, Yushi Wu, Yue Yang, Xianjun Luo, Xiao Yu, Wenchao Zhao, Xujiang Liu, Yanchi Gu, Quanquan Chen, Haifeng Wang, Wei Cheng, Wei |
| author_facet | Xiao, Yijia Jin, Yiqiao Bai, Yushi Wu, Yue Yang, Xianjun Luo, Xiao Yu, Wenchao Zhao, Xujiang Liu, Yanchi Gu, Quanquan Chen, Haifeng Wang, Wei Cheng, Wei |
| contents | The proliferation of Large Language Models (LLMs) has driven considerable interest in fine-tuning them with domain-specific data to create specialized language models. Nevertheless, such domain-specific fine-tuning data often contains contextually sensitive personally identifiable information (PII). Direct fine-tuning of LLMs on this data without privacy protection poses a risk of data leakage of sensitive PII during inference time. To address this challenge, we introduce Contextual Privacy Protection Language Models (PrivacyMind), a novel paradigm for fine-tuning LLMs that effectively injects domain-specific knowledge while safeguarding inference-time data privacy. Our work offers a theoretical analysis for model design and benchmarks various techniques such as corpus curation, penalty-based unlikelihood in training loss, instruction-based tuning, etc. Extensive experiments across diverse datasets and scenarios demonstrate the effectiveness of our approaches. In particular, instruction tuning with both positive and negative examples stands out as a promising method, effectively protecting private data while enhancing the model's knowledge. Our work underscores the potential for Large Language Models as robust contextual privacy protection learners. The complete code and data for the work can be found at https://github.com/Yijia-Xiao/PrivacyMind. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_02469 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | PrivacyMind: Large Language Models Can Be Contextual Privacy Protection Learners Xiao, Yijia Jin, Yiqiao Bai, Yushi Wu, Yue Yang, Xianjun Luo, Xiao Yu, Wenchao Zhao, Xujiang Liu, Yanchi Gu, Quanquan Chen, Haifeng Wang, Wei Cheng, Wei Computation and Language Artificial Intelligence Machine Learning The proliferation of Large Language Models (LLMs) has driven considerable interest in fine-tuning them with domain-specific data to create specialized language models. Nevertheless, such domain-specific fine-tuning data often contains contextually sensitive personally identifiable information (PII). Direct fine-tuning of LLMs on this data without privacy protection poses a risk of data leakage of sensitive PII during inference time. To address this challenge, we introduce Contextual Privacy Protection Language Models (PrivacyMind), a novel paradigm for fine-tuning LLMs that effectively injects domain-specific knowledge while safeguarding inference-time data privacy. Our work offers a theoretical analysis for model design and benchmarks various techniques such as corpus curation, penalty-based unlikelihood in training loss, instruction-based tuning, etc. Extensive experiments across diverse datasets and scenarios demonstrate the effectiveness of our approaches. In particular, instruction tuning with both positive and negative examples stands out as a promising method, effectively protecting private data while enhancing the model's knowledge. Our work underscores the potential for Large Language Models as robust contextual privacy protection learners. The complete code and data for the work can be found at https://github.com/Yijia-Xiao/PrivacyMind. |
| title | PrivacyMind: Large Language Models Can Be Contextual Privacy Protection Learners |
| topic | Computation and Language Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2310.02469 |