Output Length Effect on DeepSeek-R1's Safety in Forced Thinking
Fuente:
arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866910856957132800 |
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| author | Li, Xuying Li, Zhuo Kosuga, Yuji Bian, Victor |
| author_facet | Li, Xuying Li, Zhuo Kosuga, Yuji Bian, Victor |
| contents | Large Language Models (LLMs) have demonstrated strong reasoning capabilities, but their safety under adversarial conditions remains a challenge. This study examines the impact of output length on the robustness of DeepSeek-R1, particularly in Forced Thinking scenarios. We analyze responses across various adversarial prompts and find that while longer outputs can improve safety through self-correction, certain attack types exploit extended generations. Our findings suggest that output length should be dynamically controlled to balance reasoning effectiveness and security. We propose reinforcement learning-based policy adjustments and adaptive token length regulation to enhance LLM safety. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_01923 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Output Length Effect on DeepSeek-R1's Safety in Forced Thinking Li, Xuying Li, Zhuo Kosuga, Yuji Bian, Victor Computation and Language Artificial Intelligence Large Language Models (LLMs) have demonstrated strong reasoning capabilities, but their safety under adversarial conditions remains a challenge. This study examines the impact of output length on the robustness of DeepSeek-R1, particularly in Forced Thinking scenarios. We analyze responses across various adversarial prompts and find that while longer outputs can improve safety through self-correction, certain attack types exploit extended generations. Our findings suggest that output length should be dynamically controlled to balance reasoning effectiveness and security. We propose reinforcement learning-based policy adjustments and adaptive token length regulation to enhance LLM safety. |
| title | Output Length Effect on DeepSeek-R1's Safety in Forced Thinking |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2503.01923 |