Mimicking User Data: On Mitigating Fine-Tuning Risks in Closed Large Language Models

• URL: http://arxiv.org/abs/2406.10288v2
• Date: Mon, 1 Jul 2024 10:17:58 GMT
• Title: Mimicking User Data: On Mitigating Fine-Tuning Risks in Closed Large Language Models
• Authors: Francisco Eiras, Aleksandar Petrov, Phillip H. S. Torr, M. Pawan Kumar, Adel Bibi,
• Abstract summary: Fine-tuning large language models on small datasets can enhance their performance on specific downstream tasks. Malicious actors can subtly manipulate the structure of almost any task-specific dataset to foster significantly more dangerous model behaviors. We propose a novel mitigation strategy that mixes in safety data which mimics the task format and prompting style of the user data.
• Score: 53.50543146583101
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Fine-tuning large language models on small, high-quality datasets can enhance their performance on specific downstream tasks. Recent research shows that fine-tuning on benign, instruction-following data can inadvertently undo the safety alignment process and increase a model's propensity to comply with harmful queries. Although critical, understanding and mitigating safety risks in well-defined tasks remains distinct from the instruction-following context due to structural differences in the data. Our work addresses the gap in our understanding of these risks across diverse types of data in closed models - where providers control how user data is utilized in the fine-tuning process. We demonstrate how malicious actors can subtly manipulate the structure of almost any task-specific dataset to foster significantly more dangerous model behaviors, while maintaining an appearance of innocuity and reasonable downstream task performance. To address this issue, we propose a novel mitigation strategy that mixes in safety data which mimics the task format and prompting style of the user data, showing this is more effective than existing baselines at re-establishing safety alignment while maintaining similar task performance.

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