Fundamental Safety-Capability Trade-offs in Fine-tuning Large Language Models
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866917968678486016 |
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| author | Chen, Pin-Yu Shen, Han Das, Payel Chen, Tianyi |
| author_facet | Chen, Pin-Yu Shen, Han Das, Payel Chen, Tianyi |
| contents | Fine-tuning Large Language Models (LLMs) on some task-specific datasets has been a primary use of LLMs. However, it has been empirically observed that this approach to enhancing capability inevitably compromises safety, a phenomenon also known as the safety-capability trade-off in LLM fine-tuning. This paper presents a theoretical framework for understanding the interplay between safety and capability in two primary safety-aware LLM fine-tuning strategies, providing new insights into the effects of data similarity, context overlap, and alignment loss landscape. Our theoretical results characterize the fundamental limits of the safety-capability trade-off in LLM fine-tuning, which are also validated by numerical experiments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_20807 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Fundamental Safety-Capability Trade-offs in Fine-tuning Large Language Models Chen, Pin-Yu Shen, Han Das, Payel Chen, Tianyi Machine Learning Artificial Intelligence Computation and Language Fine-tuning Large Language Models (LLMs) on some task-specific datasets has been a primary use of LLMs. However, it has been empirically observed that this approach to enhancing capability inevitably compromises safety, a phenomenon also known as the safety-capability trade-off in LLM fine-tuning. This paper presents a theoretical framework for understanding the interplay between safety and capability in two primary safety-aware LLM fine-tuning strategies, providing new insights into the effects of data similarity, context overlap, and alignment loss landscape. Our theoretical results characterize the fundamental limits of the safety-capability trade-off in LLM fine-tuning, which are also validated by numerical experiments. |
| title | Fundamental Safety-Capability Trade-offs in Fine-tuning Large Language Models |
| topic | Machine Learning Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2503.20807 |