没有免费午餐:获取增强生成破坏了LLM的公平性,即使对于警的用户也是如此
Mengxuan Hu1, Hongyi Wu1, Ronghang Zhu2
1University of Virginia.
概括
检索增强生成 (RAG) 可以在大型语言模型 (LLM) 中引入公平性问题,即使有被审查的数据. 这项研究揭示了RAG.
科学领域:
- 人工智能的人工智能
- 自然语言处理自然语言处理.
- 机器学习伦理学 机器学习伦理学
背景情况:
- 检索增强生成 (RAG) 是一种用于增强大型语言模型 (LLM) 的流行的技术.
- 在不需要昂贵的再培训的情况下,RAG提高了LLM事实性和特定领域的知识.
- 对LLM成果的公平性和潜在偏见的担忧仍然存在.
研究的目的:
- 调查使用RAG的公平性成本和影响.
- 分析RAG中不同级别的数据审查如何影响公平性结果.
- 评估RAG系统对与公平相关的攻击的脆弱性.
主要方法:
- 提出了基于用户公平意识的三级威胁模型.
- 使用RAG对未经审查,部分审查和完全审查的外部数据集进行实验.
- 在不微调或重新培训LLMs的情况下评估了公平性影响.
主要成果:
- 在没有模型修改的情况下,RAG中的公平性调整很容易受到损害.
- 即使是完全审查和据称无偏的外部数据集也可以通过RAG.导致偏见的LLM输出.
- RAG系统表现出易受公平性破坏的影响.
结论:
- 目前的LLM对齐方法可能对RAG系统来说不足.
- RAG引入了独特的公平挑战,需要新的缓解策略.
- 进一步的研究对于开发基于RAG的LLMs的强有力的公平保障至关重要.
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