在LLM培训中使用基于共识的奖励来缓解恶意RLHF反的框架
Zafaryab Haider1, Md Hafizur Rahman2, Vijay Devabhaktuni3
1Department of Electrical and Computer Engineering (ECE), University of Maine, Orono, ME, USA. zafaryab.haider@maine.edu.
Scientific reports
|March 18, 2025
概括
一个名为COBRA的新框架解决了使用人类反 (RLHF) 强化学习训练大型语言模型 (LLM) 的安全风险. 科布拉有效地过出恶意的人类反,提高了LLM在现实应用中的性能和安全性.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 自然语言处理自然语言处理.
背景情况:
- 大型语言模型 (LLM) 在各个行业越来越多地被采用,但面临着安全和隐私方面的挑战.
- 强化学习从人类反 (RLHF) 对于LLM培训至关重要,传授人类的品质.
- RLHF过程容易受到恶意反的影响,可能会降低LLM的性能并导致有害的输出.
研究的目的:
- 提出一个新的框架,COBRA (基于共识的奖励),以减轻RLHF的恶意反.
- 在混合信任环境中增强LLM培训性能和稳定性.
- 为了验证COBRA对最先进的方法的有效性.
主要方法:
- 开发了COBRA框架,这是一个基于共识的技术,用于在RLHF期间过噪音的人类反.
- 通过使用各种LLM模型 (例如,GPT-2 XL) 评估了COBRA对情绪分析和对话任务的用例.
- 将COBRA的性能与标准RLHF和先前的方法进行比较 (Coste等. ) 的情况.
主要成果:
- COBRA显著提高了LLM的性能,通过[公式:查看文本]进行对话任务和[公式:查看文本]进行情绪分析来优于不受保护的奖励生成.
- 量化比较显示,COBRA实现了最先进的性能,特别是使用更少的奖励模型.
- 在较少的奖励模型中,COBRA证明了奖励准确度的提高 ([公式:见文本]).
结论:
- COBRA有效地中和了RLHF中的恶意反,提高了LLM培训结果.
- 拟议的框架为在关键应用中安全可靠的LLM开发提供了强大的解决方案.
- 科巴在确保法学士培训数据的完整性和质量方面取得了重大进展.
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